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: "neural generative question answering"
  Abstract: This paper presents an end-to-end neural network model, named Neural Generative Question Answering (GENQA), that can generate answers to simple factoid questions, based on the facts in a knowledge-base. More specifically, the model is built on the encoder-decoder framework for sequence-to-sequence learning, while equipped with the ability to enquire the knowledge-base, and is trained on a corpus of question-answer pairs, with their associated triples in the knowledge-base. Empirical study shows the proposed model can effectively deal with the variations of questions and answers, and generate right and natural answers by referring to the facts in the knowledge-base. The experiment on question answering demonstrates that the proposed model can outperform an embedding-based QA model as well as a neural dialogue model trained on the same data.

Connected through hyperedge HE-1 (induced by source paper "revisiting paraphrase question generator using pairwise discriminator"):
  with members:
    - "learning semantic sentence embeddings using sequential pair wise discriminator"
    - "toward controlled generation of text"
    - "learning to ask neural question generation for reading comprehension"
    - "adversarial learning for neural dialogue generation"
    - "long text generation via adversarial training with leaked information"
    - "a deep generative framework for paraphrase generation"
    - "paraphrase generation with deep reinforcement learning"
    - "recurrent topic transition gan for visual paragraph generation"

Connected through hyperedge HE-2 (induced by source paper "bert post training for review reading comprehension and aspect based sentiment analysis"):
  with members:
    - "exploiting coarse to fine task transfer for aspect level sentiment classification"
    - "double embeddings and cnn based sequence labeling for aspect extraction"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "exploiting document knowledge for aspect level sentiment classification"
    - "know what you don t know unanswerable questions for squad"
    - "deep contextualized word representations"
    - "coqa a conversational question answering challenge"
    - "drcd a chinese machine reading comprehension dataset"

Connected through hyperedge HE-3 (induced by source paper "review conversational reading comprehension"):
  with members:
    - "sdnet contextualized attention based deep network for conversational question answering"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "know what you don t know unanswerable questions for squad"
    - "deep contextualized word representations"
    - "coqa a conversational question answering challenge"
    - "drcd a chinese machine reading comprehension dataset"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "quac question answering in context"

Connected through hyperedge HE-4 (induced by source paper "dykgchat benchmarking dialogue generation grounding on dynamic knowledge graphs"):
  with members:
    - "reinforcewalk learning to walk in graph with monte carlo tree search"
    - "building dynamic knowledge graphs from text using machine reading comprehension"
    - "open domain question answering using early fusion of knowledge bases and text"
    - "variational knowledge graph reasoning"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "key value retrieval networks for task oriented dialogue"
    - "a knowledge grounded neural conversation model"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"

Connected through hyperedge HE-5 (induced by source paper "adapting sequence to sequence models for text normalization in social media"):
  with members:
    - "learning to compute word embeddings on the fly"
    - "improving text normalization by optimizing nearest neighbor matching"
    - "a nested attention neural hybrid model for grammatical error correction"
    - "get to the point summarization with pointer generator networks"
    - "bpemb tokenization free pre trained subword embeddings in 275 languages"
    - "monoise modeling noise using a modular normalization system"
    - "synthetic and natural noise both break neural machine translation"
    - "abstractive text summarization using sequence to sequence rnns and beyond"

Connected through hyperedge HE-6 (induced by source paper "answer based adversarial training for generating clarification questions"):
  with members:
    - "learning to ask good questions ranking clarification questions using neural expected value of perfect information"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "learning to ask neural question generation for reading comprehension"
    - "adversarial learning for neural dialogue generation"
    - "shakespearizing modern language using copy enriched sequence to sequence models"
    - "a persona based neural conversation model"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "abstractive text summarization using sequence to sequence rnns and beyond"

Connected through hyperedge HE-7 (induced by source paper "collaborative multi agent dialogue model training via reinforcement learning"):
  with members:
    - "textworld a learning environment for text based games"
    - "switch based active deep dyna q efficient adaptive planning for task completion dialogue policy learning"
    - "neural approaches to conversational ai"
    - "counterfactual multi agent policy gradients"
    - "sample efficient actor critic reinforcement learning with supervised data for dialogue management"
    - "iterative policy learning in end to end trainable task oriented neural dialog models"
    - "end to end task completion neural dialogue systems"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "review driven answer generation for product related questions in e commerce"):
  with members:
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "flexible and creative chinese poetry generation using neural memory"
    - "modelling domain relationships for transfer learning on retrieval based question answering systems in e commerce"
    - "convolutional sequence to sequence learning"
    - "interactive attention networks for aspect level sentiment classification"
    - "deep reinforcement learning for dialogue generation"
    - "sequence to backward and forward sequences a content introducing approach to generative short text conversation"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"

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)