Discovering the creative potentials of a random signal to various artistic expressions in aesthetic and conceptual richness is a ground for the recent success of generative machine learning as a way of art creation. To understand the new artistic medium better, in this work, we comprehensively analyze AI-generated art within the context of human art heritage using our dataset, "ArtConstellation," comprising annotations for 6,000 WikiArt and 3,200 AI-generated artworks. After training various generative models, we compare the produced art samples with WikiArt data using the last hidden layer of a deep-CNN trained for style classification. By interpreting neural representations with important artistic concepts like Wölfflin’s principles, we find that AI-generated artworks align with modern period art concepts (1800 - 2000). Out-Of-Distribution (OOD) and In-Distribution (ID) detection in CLIP space reveal that AI-generated art is ID to human art with landscapes and geometric abstract figures but OOD with deformed and twisted figures, showcasing unique characteristics. A human survey on emotional experience indicates color composition and familiar subjects as key factors in likability and emotions. We introduce our methodologies and dataset, "ArtNeural-Constellation," as a framework for contrasting human and AI-generated art. Code and data are available here.