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: "dp gan diversity promoting generative adversarial network for generating informative and diversified text"
  Abstract: Existing text generation methods tend to produce repeated and "boring" expressions. To tackle this problem, we propose a new text generation model, called Diversity-Promoting Generative Adversarial Network (DP-GAN). The proposed model assigns low reward for repeated text and high reward for "novel" text, encouraging the generator to produce diverse and informative text. Moreover, we propose a novel language-model based discriminator, which can better distinguish novel text from repeated text without the saturation problem compared with existing classifier-based discriminators. The experimental results on review generation and dialogue generation tasks demonstrate that our method can generate substantially more diverse and informative text than existing baseline methods. The code is available at this https URL

Connected through hyperedge HE-1 (induced by source paper "autoregressive text generation beyond feedback loops"):
  with members:
    - "on accurate evaluation of gans for language generation"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "large scale classification of structured objects using a crf with deep class embedding"
    - "deep state space models for unconditional word generation"
    - "language gans falling short"
    - "structured attention networks"
    - "advances in variational inference"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "hscjn a holistic semantic constraint joint network for diverse response generation"):
  with members:
    - "entropy enhanced multimodal attention model for scene aware dialogue generation"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "a knowledge grounded neural conversation model"
    - "dailydialog a manually labelled multi turn dialogue dataset"
    - "vocabulary selection strategies for neural machine translation"
    - "neural machine translation with word predictions"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "a simple fast diverse decoding algorithm for neural generation"

Connected through hyperedge HE-3 (induced by source paper "on accurate evaluation of gans for language generation"):
  with members:
    - "eval all trust a few do wrong to none comparing sentence generation models"
    - "neural text generation past present and beyond"
    - "universal sentence encoder"
    - "a hybrid convolutional variational autoencoder for text generation"
    - "began boundary equilibrium generative adversarial networks"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "progressive growing of gans for improved quality stability and variation"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "synthesizing tabular data using generative adversarial networks"):
  with members:
    - "gain missing data imputation using generative adversarial nets"
    - "correlated discrete data generation using adversarial training"
    - "began boundary equilibrium generative adversarial networks"
    - "mida multiple imputation using denoising autoencoders"
    - "generative adversarial networks for electronic health records a framework for exploring and evaluating methods for predicting drug induced laboratory test trajectories"
    - "progressive growing of gans for improved quality stability and variation"
    - "generating multi label discrete patient records using generative adversarial networks"
    - "segan speech enhancement generative adversarial network"

Connected through hyperedge HE-5 (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"
    - "cot cooperative training for generative modeling of discrete data"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-6 (induced by source paper "neural text generation past present and beyond"):
  with members:
    - "texygen a benchmarking platform for text generation models"
    - "toward controlled generation of text"
    - "neural text generation a practical guide"
    - "adversarial generation of natural language"
    - "adversarial multi task learning for text classification"
    - "maximum likelihood augmented discrete generative adversarial networks"
    - "long text generation via adversarial training with leaked information"
    - "seqgan sequence generative adversarial nets with policy gradient"

Connected through hyperedge HE-7 (induced by source paper "regularizing output distribution of abstractive chinese social media text summarization for improved semantic consistency"):
  with members:
    - "a hierarchical end to end model for jointly improving text summarization and sentiment classification"
    - "word embedding attention network generating words by querying distributed word representations for paraphrase generation"
    - "decoding history based adaptive control of attention for neural machine translation"
    - "get to the point summarization with pointer generator networks"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "softtarget regularization an effective technique to reduce over fitting in neural networks"
    - "supervised attentions for neural machine translation"
    - "a neural attention model for abstractive sentence summarization"

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.CL(Computation and Language)