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 argument generation augmented with externally retrieved evidence"
  Abstract: High quality arguments are essential elements for human reasoning and decision-making processes. However, effective argument construction is a challenging task for both human and machines. In this work, we study a novel task on automatically generating arguments of a different stance for a given statement. We propose an encoder-decoder style neural network-based argument generation model enriched with externally retrieved evidence from Wikipedia. Our model first generates a set of talking point phrases as intermediate representation, followed by a separate decoder producing the final argument based on both input and the keyphrases. Experiments on a large-scale dataset collected from Reddit show that our model constructs arguments with more topic-relevant content than a popular sequence-to-sequence generation model according to both automatic evaluation and human assessments.

Connected through hyperedge HE-1 (induced by source paper "keeping notes conditional natural language generation with a scratchpad mechanism"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "bottom up abstractive summarization"
    - "zero shot question generation from knowledge graphs for unseen predicates and entity types"
    - "learning to ask neural question generation for reading comprehension"
    - "neural text generation a practical guide"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "sentence level content planning and style specification for neural text generation"):
  with members:
    - "text generation from knowledge graphs with graph transformers"
    - "argument generation with retrieval planning and realization"
    - "evaluating the state of the art of end to end natural language generation the e2e nlg challenge"
    - "shaping the narrative arc an information theoretic approach to collaborative dialogue"
    - "step by step separating planning from realization in neural data to text generation"
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"
    - "hierarchical neural story generation"
    - "a graph to sequence model for amr to text generation"

Connected through hyperedge HE-3 (induced by source paper "determining relative argument specificity and stance for complex argumentative structures"):
  with members:
    - "domain agnostic real valued specificity prediction"
    - "exploring the role of prior beliefs for argument persuasion"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "predicting specificity in classroom discussion"
    - "debbie the debate bot of the future"
    - "argument mining with structured svms and rnns"
    - "neural network based abstract generation for opinions and arguments"
    - "aligning books and movies towards story like visual explanations by watching movies and reading books"

Connected through hyperedge HE-4 (induced by source paper "a dataset of general purpose rebuttal"):
  with members:
    - "argument generation with retrieval planning and realization"
    - "argument invention from first principles"
    - "training millions of personalized dialogue agents"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a survey on dialogue systems recent advances and new frontiers"
    - "a network based end to end trainable task oriented dialogue system"
    - "topic aware neural response generation"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-5 (induced by source paper "argument generation with retrieval planning and realization"):
  with members:
    - "bottom up abstractive summarization"
    - "winning on the merits the joint effects of content and style on debate outcomes"
    - "a diversity promoting objective function for neural conversation models"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-6 (induced by source paper "riposte a large corpus of counter arguments"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "the argument reasoning comprehension task identification and reconstruction of implicit warrants"

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)