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: "global versus localized generative adversarial nets"
  Abstract: In this paper, we present a novel localized Generative Adversarial Net (GAN) to learn on the manifold of real data. Compared with the classic GAN that {\em globally} parameterizes a manifold, the Localized GAN (LGAN) uses local coordinate charts to parameterize distinct local geometry of how data points can transform at different locations on the manifold. Specifically, around each point there exists a {\em local} generator that can produce data following diverse patterns of transformations on the manifold. The locality nature of LGAN enables local generators to adapt to and directly access the local geometry without need to invert the generator in a global GAN. Furthermore, it can prevent the manifold from being locally collapsed to a dimensionally deficient tangent subspace by imposing an orthonormality prior between tangents. This provides a geometric approach to alleviating mode collapse at least locally on the manifold by imposing independence between data transformations in different tangent directions. We will also demonstrate the LGAN can be applied to train a robust classifier that prefers locally consistent classification decisions on the manifold, and the resultant regularizer is closely related with the Laplace-Beltrami operator. Our experiments show that the proposed LGANs can not only produce diverse image transformations, but also deliver superior classification performances.

Connected through hyperedge HE-1 (induced by source paper "end to end graph based deep semi supervised learning"):
  with members:
    - "randaugment practical automated data augmentation with a reduced search space"
    - "remixmatch semi supervised learning with distribution alignment and augmentation anchoring"
    - "label propagation for deep semi supervised learning"
    - "mixmatch a holistic approach to semi supervised learning"
    - "there are many consistent explanations of unlabeled data why you should average"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised learning via compact latent space clustering"
    - "realistic evaluation of deep semi supervised learning algorithms"

Connected through hyperedge HE-2 (induced by source paper "aet vs aed unsupervised representation learning by auto encoding transformations rather than data"):
  with members:
    - "deep clustering for unsupervised learning of visual features"
    - "unsupervised representation learning by predicting image rotations"
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "scaling the scattering transform deep hybrid networks"
    - "representation learning by learning to count"
    - "learning representations for automatic colorization"
    - "adversarial feature learning"
    - "unsupervised learning of visual representations by solving jigsaw puzzles"

Connected through hyperedge HE-3 (induced by source paper "decouple learning for parameterized image operators"):
  with members:
    - "generating diverse and accurate visual captions by comparative adversarial learning"
    - "s3d single shot multi span detector via fully 3d convolutional networks"
    - "stereoscopic neural style transfer"
    - "revisiting rcnn on awakening the classification power of faster rcnn"
    - "density aware single image de raining using a multi stream dense network"
    - "deep exemplar based colorization"
    - "clearing the skies a deep network architecture for single image rain removal"
    - "temporal context network for activity localization in videos"

Connected through hyperedge HE-4 (induced by source paper "tangent normal adversarial regularization for semi supervised learning"):
  with members:
    - "semi supervised learning with gans revisiting manifold regularization"
    - "semi supervised learning via compact latent space clustering"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "good semi supervised learning that requires a bad gan"
    - "semi supervised learning with gans manifold invariance with improved inference"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "triple generative adversarial nets"
    - "smooth neighbors on teacher graphs for semi supervised learning"

Connected through hyperedge HE-5 (induced by source paper "loss sensitive generative adversarial networks on lipschitz densities"):
  with members:
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "gradient descent gan optimization is locally stable"
    - "generalization and equilibrium in generative adversarial nets gans"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "energy based generative adversarial network"
    - "temporal generative adversarial nets with singular value clipping"
    - "generating images with recurrent adversarial networks"
    - "c rnn gan continuous recurrent neural networks with adversarial training"

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)