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 "towards effective rebuttal listening comprehension using corpus wide claim mining"):
  Abstract: Engaging in a live debate requires, among other things, the ability to effectively rebut arguments claimed by your opponent. In particular, this requires identifying these arguments. Here, we suggest doing so by automatically mining claims from a corpus of news articles containing billions of sentences, and searching for them in a given speech. This raises the question of whether such claims indeed correspond to those made in spoken speeches. To this end, we collected a large dataset of $400$ speeches in English discussing $200$ controversial topics, mined claims for each topic, and asked annotators to identify the mined claims mentioned in each speech. Results show that in the vast majority of speeches debaters indeed make use of such claims. In addition, we present several baselines for the automatic detection of mined claims in speeches, forming the basis for future work. All collected data is freely available for research.

Includes members:
  Member "spoken squad a study of mitigating the impact of speech recognition errors on listening comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a pilot study on multiple choice machine reading comprehension for vietnamese texts")
  Member "argumentation mining in user generated web discourse":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "argulens anatomy of community opinions on usability issues using argumentation models")
  Member "a recorded debating dataset":
    also connected through other hyperedges:
      (none)
  Member "joint learning of correlated sequence labelling tasks using bidirectional recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "factored latent dynamic conditional random fields for single and multi label sequence modeling")
  Member "towards machine comprehension of spoken content initial toefl listening comprehension test by machine":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mmm multi stage multi task learning for multi choice reading comprehension")
  Member "parsing argumentation structures in persuasive essays":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "argulens anatomy of community opinions on usability issues using argumentation models")
  Member "hierarchical attention model for improved machine comprehension of spoken content":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "odsqa open domain spoken question answering dataset")
  Member "efficient estimation of word representations in vector space":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on contextual embeddings")

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)