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: "guiding infogan with semi supervision"
  Abstract: In this paper we propose a new semi-supervised GAN architecture (ss-InfoGAN) for image synthesis that leverages information from few labels (as little as 0.22%, max. 10% of the dataset) to learn semantically meaningful and controllable data representations where latent variables correspond to label categories. The architecture builds on Information Maximizing Generative Adversarial Networks (InfoGAN) and is shown to learn both continuous and categorical codes and achieves higher quality of synthetic samples compared to fully unsupervised settings. Furthermore, we show that using small amounts of labeled data speeds-up training convergence. The architecture maintains the ability to disentangle latent variables for which no labels are available. Finally, we contribute an information-theoretic reasoning on how introducing semi-supervision increases mutual information between synthetic and real data.

Connected through hyperedge HE-1 (induced by source paper "unpaired pose guided human image generation"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "soft gated warping gan for pose guided person image synthesis"
    - "synthesizing images of humans in unseen poses"
    - "unsupervised person image synthesis in arbitrary poses"
    - "toward characteristic preserving image based virtual try on network"
    - "multimodal unsupervised image to image translation"
    - "dense pose transfer"
    - "everybody dance now"

Connected through hyperedge HE-2 (induced by source paper "using latent codes for class imbalance problem in unsupervised domain adaptation"):
  with members:
    - "learning to transfer examples for partial domain adaptation"
    - "cgans with projection discriminator"
    - "improving adversarial discriminative domain adaptation"
    - "open set domain adaptation by backpropagation"
    - "deep visual domain adaptation a survey"
    - "importance weighted adversarial nets for partial domain adaptation"
    - "partial adversarial domain adaptation"
    - "learning to reweight examples for robust deep learning"

Connected through hyperedge HE-3 (induced by source paper "disentanglement based active learning"):
  with members:
    - "bayesian generative active deep learning"
    - "one pixel attack for fooling deep neural networks"
    - "generative adversarial network in medical imaging a review"
    - "how good is my gan"
    - "label noise robust generative adversarial networks"
    - "generative adversarial active learning"
    - "structured generative adversarial networks"
    - "triple generative adversarial nets"

Connected through hyperedge HE-4 (induced by source paper "bridging disentanglement with independence and conditional independence via mutual information for representation learning"):
  with members:
    - "spatial broadcast decoder a simple architecture for learning disentangled representations in vaes"
    - "on the fairness of disentangled representations"
    - "competitive training of mixtures of independent deep generative models"
    - "curiosity driven exploration of learned disentangled goal spaces"
    - "improving generalization for abstract reasoning tasks using disentangled feature representations"
    - "recent advances in autoencoder based representation learning"
    - "towards a definition of disentangled representations"
    - "disentangling factors of variation in deep representations using 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)