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: "cot cooperative training for generative modeling of discrete data"
  Abstract: In this paper, we study the generative models of sequential discrete data. To tackle the exposure bias problem inherent in maximum likelihood estimation (MLE), generative adversarial networks (GANs) are introduced to penalize the unrealistic generated samples. To exploit the supervision signal from the discriminator, most previous models leverage REINFORCE to address the non-differentiable problem of sequential discrete data. However, because of the unstable property of the training signal during the dynamic process of adversarial training, the effectiveness of REINFORCE, in this case, is hardly guaranteed. To deal with such a problem, we propose a novel approach called Cooperative Training (CoT) to improve the training of sequence generative models. CoT transforms the min-max game of GANs into a joint maximization framework and manages to explicitly estimate and optimize Jensen-Shannon divergence. Moreover, CoT works without the necessity of pre-training via MLE, which is crucial to the success of previous methods. In the experiments, compared to existing state-of-the-art methods, CoT shows superior or at least competitive performance on sample quality, diversity, as well as training stability.

Connected through hyperedge HE-1 (induced by source paper "triple to text converting rdf triples into high quality natural languages via optimizing an inverse kl divergence"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "texygen a benchmarking platform for text generation models"
    - "adversarially regularized autoencoders"
    - "neural wikipedian generating textual summaries from knowledge base triples"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "get to the point summarization with pointer generator networks"
    - "adversarial learning for neural dialogue generation"

Connected through hyperedge HE-2 (induced by source paper "evaluating computational language models with scaling properties of natural language"):
  with members:
    - "taylor s law for human linguistic sequences"
    - "an analysis of neural language modeling at multiple scales"
    - "texygen a benchmarking platform for text generation models"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "on the state of the art of evaluation in neural language models"
    - "do neural nets learn statistical laws behind natural language"
    - "adversarial generation of natural language"
    - "regularizing and optimizing lstm language models"

Connected through hyperedge HE-3 (induced by source paper "language gans falling short"):
  with members:
    - "on accurate evaluation of gans for language generation"
    - "eval all trust a few do wrong to none comparing sentence generation models"
    - "neural text generation past present and beyond"
    - "analyzing uncertainty in neural machine translation"
    - "adversarial text generation via feature mover s distance"
    - "towards diverse text generation with inverse reinforcement learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "texygen a benchmarking platform for text generation models"

Connected through hyperedge HE-4 (induced by source paper "generative adversarial nets for information retrieval fundamentals and advances"):
  with members:
    - "neural text generation past present and beyond"
    - "texygen a benchmarking platform for text generation models"
    - "kbgan adversarial learning for knowledge graph embeddings"
    - "neural collaborative filtering"
    - "adversarial ranking for language generation"
    - "maximum likelihood augmented discrete generative adversarial networks"
    - "graphgan graph representation learning with generative adversarial nets"
    - "long text generation via adversarial training with leaked information"

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)