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: "structured generative adversarial networks"
  Abstract: We study the problem of conditional generative modeling based on designated semantics or structures. Existing models that build conditional generators either require massive labeled instances as supervision or are unable to accurately control the semantics of generated samples. We propose structured generative adversarial networks (SGANs) for semi-supervised conditional generative modeling. SGAN assumes the data x is generated conditioned on two independent latent variables: y that encodes the designated semantics, and z that contains other factors of variation. To ensure disentangled semantics in y and z, SGAN builds two collaborative games in the hidden space to minimize the reconstruction error of y and z, respectively. Training SGAN also involves solving two adversarial games that have their equilibrium concentrating at the true joint data distributions p(x, z) and p(x, y), avoiding distributing the probability mass diffusely over data space that MLE-based methods may suffer. We assess SGAN by evaluating its trained networks, and its performance on downstream tasks. We show that SGAN delivers a highly controllable generator, and disentangled representations; it also establishes start-of-the-art results across multiple datasets when applied for semi-supervised image classification (1.27%, 5.73%, 17.26% error rates on MNIST, SVHN and CIFAR-10 using 50, 1000 and 4000 labels, respectively). Benefiting from the separate modeling of y and z, SGAN can generate images with high visual quality and strictly following the designated semantic, and can be extended to a wide spectrum of applications, such as style transfer.

Connected through hyperedge HE-1 (induced by source paper "high fidelity image generation with fewer labels"):
  with members:
    - "s4l self supervised semi supervised learning"
    - "revisiting self supervised visual representation learning"
    - "cgans with projection discriminator"
    - "grasp2vec learning object representations from self supervised grasping"
    - "the gan landscape losses architectures regularization and normalization"
    - "self supervised gans via auxiliary rotation loss"
    - "deep clustering for unsupervised learning of visual features"
    - "spectral normalization for generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "cluster alignment with a teacher for unsupervised domain adaptation"):
  with members:
    - "domain adaptation with asymmetrically relaxed distribution alignment"
    - "on learning invariant representation for domain adaptation"
    - "max mahalanobis linear discriminant analysis networks"
    - "image to image translation for domain adaptation"
    - "cycada cycle consistent adversarial domain adaptation"
    - "maximum classifier discrepancy for unsupervised domain adaptation"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "unsupervised domain adaptation with similarity learning"

Connected through hyperedge HE-3 (induced by source paper "multivariate information adversarial ensemble for scalable joint distribution matching"):
  with members:
    - "video to video synthesis"
    - "geometry score a method for comparing generative adversarial networks"
    - "stargan vc non parallel many to many voice conversion with star generative adversarial networks"
    - "soft gated warping gan for pose guided person image synthesis"
    - "modular generative adversarial networks"
    - "jointgan multi domain joint distribution learning with generative adversarial nets"
    - "style transfer from non parallel text by cross alignment"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-4 (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"
    - "triple generative adversarial nets"
    - "guiding infogan with semi supervision"

Connected through hyperedge HE-5 (induced by source paper "fast compressive sensing recovery using generative models with structured latent variables"):
  with members:
    - "task aware compressed sensing with generative adversarial networks"
    - "triple generative adversarial nets"
    - "an inner loop free solution to inverse problems using deep neural networks"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep learning face attributes in the wild"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "toward characteristic preserving image based virtual try on network"):
  with members:
    - "real to virtual domain unification for end to end autonomous driving"
    - "viton an image based virtual try on network"
    - "dual motion gan for future flow embedded video prediction"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "pose guided person image generation"
    - "convolutional neural network architecture for geometric matching"
    - "a generative model of people in clothing"
    - "be your own prada fashion synthesis with structural coherence"

Connected through hyperedge HE-7 (induced by source paper "soft gated warping gan for pose guided person image synthesis"):
  with members:
    - "synthesizing images of humans in unseen poses"
    - "unsupervised person image synthesis in arbitrary poses"
    - "toward characteristic preserving image based virtual try on network"
    - "a variational u net for conditional appearance and shape generation"
    - "pose robust face recognition via deep residual equivariant mapping"
    - "viton an image based virtual try on network"
    - "dual motion gan for future flow embedded video prediction"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"

Connected through hyperedge HE-8 (induced by source paper "mode matching in gans through latent space learning and inversion"):
  with members:
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "clustergan latent space clustering in generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "multi agent diverse generative adversarial networks"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "learning to discover cross domain relations with 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.LG(Machine Learning)