human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "image aesthetic assessment an experimental survey"):
  Abstract: This article reviews recent computer vision techniques used in the assessment of image aesthetic quality. Image aesthetic assessment aims at computationally distinguishing high-quality from low-quality photos based on photographic rules, typically in the form of binary classification or quality scoring. A variety of approaches has been proposed in the literature to try to solve this challenging problem. In this article, we summarize these approaches based on visual feature types (hand-crafted features and deep features) and evaluation criteria (data set characteristics and evaluation metrics). The main contributions and novelties of the reviewed approaches are highlighted and discussed. In addition, following the emergence of deep-learning techniques, we systematically evaluate recent deep-learning settings that are useful for developing a robust deep model for aesthetic scoring.

Includes members:
  Member "hybrid cnn and dictionary based models for scene recognition and domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "semantic aware scene recognition")
  Member "visual aesthetic quality assessment with multi task deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cross connected networks for multi task learning of detection and segmentation")
  Member "inception v4 inception resnet and the impact of residual connections on learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "brain inspired deep networks for image aesthetics assessment":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep cross networks with aesthetic preference for cross domain recommendation")
  Member "learning deep representation for face alignment with auxiliary attributes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "robust facial landmark detection via aggregation on geometrically manipulated faces")
  Member "describing human aesthetic perception by deeply learned attributes from flickr":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an end to end neural network for image cropping by learning composition from aesthetic photos")
  Member "polynet a pursuit of structural diversity in very deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "accuracy vs complexity a trade off in visual question answering models")
  Member "photo aesthetics ranking network with attributes and content adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "aesthetic quality assessment for group photograph")

Question: Please tell me which class the source paper belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.CV(Computer Vision and Pattern Recognition)