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 "will people like your image"):
  Abstract: The wide distribution of digital devices as well as cheap storage allow us to take series of photos making sure not to miss any specific beautiful moment. Thereby, the huge and constantly growing image assembly makes it quite time-consuming to manually pick the best shots afterwards. Even more challenging, finding the most aesthetically pleasing images that might also be worth sharing is a largely subjective task in which general rules rarely apply. Nowadays, online platforms allow users to "like" or favor certain content with a single click. As we aim to predict the aesthetic quality of images, we now make use of such multi-user agreements. More precisely, we assemble a large data set of 380K images with associated meta information and derive a score to rate how visually pleasing a given photo is. predict the aesthetic quality of any arbitrary image or video, we transfer the Our proposed model of aesthetics is validated in a user study. We demonstrate our results on applications for resorting photo collections, capturing the best shot on mobile devices and aesthetic key-frame extraction from videos.

Includes members:
  Member "perceptual losses for real time style transfer and super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "photo aesthetics ranking network with attributes and content adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "aesthetic quality assessment for group photograph")
  Member "multimodal compact bilinear pooling for visual question answering and visual grounding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "a neural algorithm of artistic style":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deformation aware unpaired image translation for pose estimation on laboratory animals")
  Member "learning to compare image patches via convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "image matching across wide baselines from paper to practice")
  Member "going deeper with convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "striving for simplicity the all convolutional net":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "imagenet large scale visual recognition challenge":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)