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: "social media argumentation mining the quest for deliberateness in raucousness"
  Abstract: Argumentation mining from social media content has attracted increasing attention. The task is both challenging and rewarding. The informal nature of user-generated content makes the task dauntingly difficult. On the other hand, the insights that could be gained by a large-scale analysis of social media argumentation make it a very worthwhile task. In this position paper I discuss the motivation for social media argumentation mining, as well as the tasks and challenges involved.

Connected through hyperedge HE-1 (induced by source paper "answering comparative questions better than ten blue links"):
  with members:
    - "categorization of comparative sentences for argument mining"
    - "building a web scale dependency parsed corpus from commoncrawl"
    - "what is the essence of a claim cross domain claim identification"
    - "understanding and detecting supporting arguments of diverse types"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-2 (induced by source paper "categorizing comparative sentences"):
  with members:
    - "answering comparative questions better than ten blue links"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "building a web scale dependency parsed corpus from commoncrawl"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "what is the essence of a claim cross domain claim identification"
    - "xgboost a scalable tree boosting system"
    - "learning distributed representations of sentences from unlabelled data"

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)