human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "one shot texture segmentation"
  Abstract: We introduce one-shot texture segmentation: the task of segmenting an input image containing multiple textures given a patch of a reference texture. This task is designed to turn the problem of texture-based perceptual grouping into an objective benchmark. We show that it is straight-forward to generate large synthetic data sets for this task from a relatively small number of natural textures. In particular, this task can be cast as a self-supervised problem thereby alleviating the need for massive amounts of manually annotated data necessary for traditional segmentation tasks. In this paper we introduce and study two concrete data sets: a dense collage of textures (CollTex) and a cluttered texturized Omniglot data set. We show that a baseline model trained on these synthesized data is able to generalize to natural images and videos without further fine-tuning, suggesting that the learned image representations are useful for higher-level vision tasks.

Connected through hyperedge HE-1 (induced by source paper "one shot texture retrieval with global context metric"):
  with members:
    - "mirrorgan learning text to image generation by redescription"
    - "meta learning for semi supervised few shot classification"
    - "deep texture manifold for ground terrain recognition"
    - "annotation free and one shot learning for instance segmentation of homogeneous object clusters"
    - "the 2018 davis challenge on video object segmentation"
    - "prototypical networks for few shot learning"
    - "learning to compare relation network for few shot learning"
    - "one shot learning for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"):
  with members:
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "the unreasonable effectiveness of texture transfer for single image super resolution"
    - "assessing shape bias property of convolutional neural networks"
    - "improving shape deformation in unsupervised image to image translation"
    - "generalisation in humans and deep neural networks"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "comparing deep neural networks against humans object recognition when the signal gets weaker"

Question: Please tell me which class the center node 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)