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: "towards principled unsupervised learning"
  Abstract: General unsupervised learning is a long-standing conceptual problem in machine learning. Supervised learning is successful because it can be solved by the minimization of the training error cost function. Unsupervised learning is not as successful, because the unsupervised objective may be unrelated to the supervised task of interest. For an example, density modelling and reconstruction have often been used for unsupervised learning, but they did not produced the sought-after performance gains, because they have no knowledge of the supervised tasks. #R##N#In this paper, we present an unsupervised cost function which we name the Output Distribution Matching (ODM) cost, which measures a divergence between the distribution of predictions and distributions of labels. The ODM cost is appealing because it is consistent with the supervised cost in the following sense: a perfect supervised classifier is also perfect according to the ODM cost. Therefore, by aggressively optimizing the ODM cost, we are almost guaranteed to improve our supervised performance whenever the space of possible predictions is exponentially large. #R##N#We demonstrate that the ODM cost works well on number of small and semi-artificial datasets using no (or almost no) labelled training cases. Finally, we show that the ODM cost can be used for one-shot domain adaptation, which allows the model to classify inputs that differ from the input distribution in significant ways without the need for prior exposure to the new domain.

Connected through hyperedge HE-1 (induced by source paper "hexagan generative adversarial nets for real world classification"):
  with members:
    - "gain missing data imputation using generative adversarial nets"
    - "imbalanced deep learning by minority class incremental rectification"
    - "which training methods for gans do actually converge"
    - "good semi supervised learning that requires a bad gan"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "triple generative adversarial nets"
    - "vigan missing view imputation with generative adversarial networks"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-2 (induced by source paper "semi supervised adversarial active learning on attributed graphs"):
  with members:
    - "attributed network embedding via subspace discovery"
    - "semi supervised learning based on generative adversarial network a comparison between good gan and bad gan approach"
    - "good semi supervised learning that requires a bad gan"
    - "active learning for graph embedding"
    - "improved techniques for training gans"
    - "semi supervised classification with graph convolutional networks"
    - "deepwalk online learning of social representations"
    - "active semi supervised learning using submodular functions"

Connected through hyperedge HE-3 (induced by source paper "learning to read by spelling towards unsupervised text recognition"):
  with members:
    - "unsupervised cipher cracking using discrete gans"
    - "unsupervised machine translation using monolingual corpora only"
    - "unsupervised sequence classification using sequential output statistics"
    - "unsupervised neural machine translation"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "precomputed real time texture synthesis with markovian generative adversarial networks"
    - "recursive recurrent nets with attention modeling for ocr in the wild"

Connected through hyperedge HE-4 (induced by source paper "nam non adversarial unsupervised domain mapping"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "non adversarial unsupervised word translation"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "progressive growing of gans for improved quality stability and variation"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "improving the improved training of wasserstein gans a consistency term and its dual effect"):
  with members:
    - "on convergence and stability of gans"
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "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"
    - "calibrating energy based generative adversarial networks"
    - "mmd gan towards deeper understanding of moment matching network"
    - "f gans in an information geometric nutshell"

Connected through hyperedge HE-6 (induced by source paper "a self training method for semi supervised gans"):
  with members:
    - "triple generative adversarial nets"
    - "generating images with recurrent adversarial networks"
    - "improved techniques for training gans"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "one sided unsupervised domain mapping"):
  with members:
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "dualgan unsupervised dual learning for image to image translation"
    - "perceptual losses for real time style transfer and super resolution"
    - "unsupervised cross domain image generation"
    - "texture networks feed forward synthesis of textures and stylized images"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"
    - "learning from simulated and unsupervised images through adversarial training"

Connected through hyperedge HE-8 (induced by source paper "the role of minimal complexity functions in unsupervised learning of semantic mappings"):
  with members:
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "global guarantees for enforcing deep generative priors by empirical risk"
    - "dualgan unsupervised dual learning for image to image translation"
    - "convolutional neural fabrics"
    - "unsupervised cross domain image generation"
    - "learning structured sparsity in deep neural networks"
    - "understanding deep learning requires rethinking generalization"

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)