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 "towards metamerism via foveated style transfer"):
  Abstract: Given the recent successes of deep learning applied to style transfer and texture synthesis, we propose a new theoretical framework to construct visual metamers: \textit{a family of perceptually identical, yet physically different images}. We review work both in neuroscience related to metameric stimuli, as well as computer vision research in style transfer. We propose our NeuroFovea metamer model that is based on a mixture of peripheral representations and style transfer forward-pass algorithms for \emph{any} image from the recent work of Adaptive Instance Normalization (Huang~\&~Belongie). Our model is parametrized by a VGG-Net versus a set of joint statistics of complex wavelet coefficients which allows us to encode images in high dimensional space and interpolate between the content and texture information. We empirically show that human observers discriminate our metamers at a similar rate as the metamers of Freeman~\&~Simoncelli (FS) In addition, our NeuroFovea metamer model gives us the benefit of near real-time generation which presents a $\times1000$ speed-up compared to previous work. Critically, psychophysical studies show that both the FS and NeuroFovea metamers are discriminable from the original images highlighting an important limitation of current metamer generation methods.

Includes members:
  Member "sideeye a generative neural network based simulator of human peripheral vision":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "180 degree outpainting from a single image")
  Member "eigen distortions of hierarchical representations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "i am going mad maximum discrepancy competition for comparing classifiers adaptively")
  Member "arbitrary style transfer in real time with adaptive instance normalization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "π nets deep polynomial neural networks")
  Member "diversified texture synthesis with feed forward networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "collaborative distillation for ultra resolution universal style transfer")
  Member "deep photo style transfer":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "parameter free style projection for arbitrary style transfer")
  Member "object detection through search with a foveated visual system":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "assessment of faster r cnn in man machine collaborative search")
  Member "perceptually optimized image rendering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "enforcing perceptual consistency on generative adversarial networks by using the normalised laplacian pyramid distance")
  Member "perceptual losses for real time style transfer and super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "gan compression efficient architectures for interactive conditional gans")

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)