human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "neural end to end learning for computational argumentation mining":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "argulens anatomy of community opinions on usability issues using argumentation models")
  Member "winning on the merits the joint effects of content and style on debate outcomes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "what changed your mind the roles of dynamic topics and discourse in argumentation process")
  Member "argument mining with structured svms and rnns":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "what changed your mind the roles of dynamic topics and discourse in argumentation process")
  Member "understanding and detecting supporting arguments of diverse types":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "tacam topic and context aware argument mining")
  Member "sequence to sequence learning as beam search optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gradient based adversarial attacks on categorical sequence models via traversing an embedded world")
  Member "neural network based abstract generation for opinions and arguments":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "what is this article about extreme summarization with topic aware convolutional neural networks")
  Member "what to talk about and how selective generation using lstms with coarse to fine alignment":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "variational template machine for data to text generation")
  Member "multi task sequence to sequence learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "attention over parameters for dialogue systems")

Question: Please tell me which class the source paper 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)