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: "adversarial training for sketch retrieval"
  Abstract: Generative Adversarial Networks (GAN) are able to learn excellent representations for unlabelled data which can be applied to image generation and scene classification. Representations learned by GANs have not yet been applied to retrieval. In this paper, we show that the representations learned by GANs can indeed be used for retrieval. We consider heritage documents that contain unlabelled Merchant Marks, sketch-like symbols that are similar to hieroglyphs. We introduce a novel GAN architecture with design features that make it suitable for sketch retrieval. The performance of this sketch-GAN is compared to a modified version of the original GAN architecture with respect to simple invariance properties. Experiments suggest that sketch-GANs learn representations that are suitable for retrieval and which also have increased stability to rotation, scale and translation compared to the standard GAN architecture.

Connected through hyperedge HE-1 (induced by source paper "learning cross modal embeddings with adversarial networks for cooking recipes and food images"):
  with members:
    - "a survey on food computing"
    - "does journaling encourage healthier choices analyzing healthy eating behaviors of food journalers"
    - "cross modal retrieval in the cooking context learning semantic text image embeddings"
    - "can a chatbot determine my diet addressing challenges of chatbot application for meal recommendation"
    - "cm gans cross modal generative adversarial networks for common representation learning"
    - "in defense of the triplet loss for person re identification"
    - "cycada cycle consistent adversarial domain adaptation"
    - "is saki delicious the food perception gap on instagram and its relation to health"

Connected through hyperedge HE-2 (induced by source paper "adversarial training in affective computing and sentiment analysis recent advances and perspectives"):
  with members:
    - "geometry contrastive gan for facial expression transfer"
    - "generative adversarial networks an overview"
    - "towards open set identity preserving face synthesis"
    - "talking face generation by conditional recurrent adversarial network"
    - "generative adversarial talking head bringing portraits to life with a weakly supervised neural network"
    - "interactive generative adversarial networks for facial expression generation in dyadic interactions"
    - "expressive speech synthesis via modeling expressions with variational autoencoder"
    - "voice impersonation using generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "generative adversarial networks an overview"):
  with members:
    - "inverting the generator of a generative adversarial network ii"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "the numerics of gans"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "precise recovery of latent vectors from generative adversarial networks"
    - "generalization and equilibrium in generative adversarial nets gans"
    - "precomputed real time texture synthesis with markovian generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "look imagine and match improving textual visual cross modal retrieval with generative models"):
  with members:
    - "stack captioning coarse to fine learning for image captioning"
    - "vse improving visual semantic embeddings with hard negatives"
    - "dual attention networks for multimodal reasoning and matching"
    - "instance aware image and sentence matching with selective multimodal lstm"
    - "generative adversarial text to image synthesis"
    - "an empirical study of language cnn for image captioning"
    - "self critical sequence training for image captioning"
    - "multimodal convolutional neural networks for matching image and sentence"

Connected through hyperedge HE-5 (induced by source paper "task specific adversarial cost function"):
  with members:
    - "one shot learning with memory augmented neural networks"
    - "energy based generative adversarial network"
    - "improved techniques for training gans"
    - "adversarial feature learning"
    - "matching networks for one shot learning"
    - "pixel recurrent neural networks"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

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)