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 "personalised aesthetics with residual adapters"):
  Abstract: The use of computational methods to evaluate aesthetics in photography has gained interest in recent years due to the popularization of convolutional neural networks and the availability of new annotated datasets. Most studies in this area have focused on designing models that do not take into account individual preferences for the prediction of the aesthetic value of pictures. We propose a model based on residual learning that is capable of learning subjective, user specific preferences over aesthetics in photography, while surpassing the state-of-the-art methods and keeping a limited number of user-specific parameters in the model. Our model can also be used for picture enhancement, and it is suitable for content-based or hybrid recommender systems in which the amount of computational resources is limited.

Includes members:
  Member "fast perceptual image enhancement":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "replacing mobile camera isp with a single deep learning model")
  Member "aesthetic based clothing recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "modeling embedding dimension correlations via convolutional neural collaborative filtering")
  Member "quantitative analysis of automatic image cropping algorithms a dataset and comparative study":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an end to end neural network for image cropping by learning composition from aesthetic photos")
  Member "image aesthetic assessment an experimental survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "composition aware image aesthetics assessment")
  Member "learning multiple visual domains with residual adapters":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on contextual embeddings")
  Member "deep cropping via attention box prediction and aesthetics assessment":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "composition aware image aesthetics assessment")
  Member "some like it hot visual guidance for preference prediction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning preference based similarities from face images using siamese multi task cnns")

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)