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 "determining relative argument specificity and stance for complex argumentative structures"):
  Abstract: Systems for automatic argument generation and debate require the ability to (1) determine the stance of any claims employed in the argument and (2) assess the specificity of each claim relative to the argument context. Existing work on understanding claim specificity and stance, however, has been limited to the study of argumentative structures that are relatively shallow, most often consisting of a single claim that directly supports or opposes the argument thesis. In this paper, we tackle these tasks in the context of complex arguments on a diverse set of topics. In particular, our dataset consists of manually curated argument trees for 741 controversial topics covering 95,312 unique claims; lines of argument are generally of depth 2 to 6. We find that as the distance between a pair of claims increases along the argument path, determining the relative specificity of a pair of claims becomes easier and determining their relative stance becomes harder.

Includes members:
  Member "domain agnostic real valued specificity prediction":
    also connected through other hyperedges:
      (none)
  Member "exploring the role of prior beliefs for argument persuasion":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "what gets echoed understanding the pointers in explanations of persuasive arguments")
  Member "neural argument generation augmented with externally retrieved evidence":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "keeping notes conditional natural language generation with a scratchpad mechanism")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "predicting specificity in classroom discussion":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep ordinal regression for pledge specificity prediction")
  Member "debbie the debate bot of the future":
    also connected through other hyperedges:
      (none)
  Member "argument mining with structured svms and rnns":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "what changed your mind the roles of dynamic topics and discourse in argumentation process")
  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")

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)