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: "understanding and detecting supporting arguments of diverse types"
  Abstract: We investigate the problem of sentence-level supporting argument detection from relevant documents for user-specified claims. A dataset containing claims and associated citation articles is collected from online debate website idebate.org. We then manually label sentence-level supporting arguments from the documents along with their types as study, factual, opinion, or reasoning. We further characterize arguments of different types, and explore whether leveraging type information can facilitate the supporting arguments detection task. Experimental results show that LambdaMART (Burges, 2010) ranker that uses features informed by argument types yields better performance than the same ranker trained without type information.

Connected through hyperedge HE-1 (induced by source paper "tacam topic and context aware argument mining"):
  with members:
    - "robust argument unit recognition and classification"
    - "deep contextualized word representations"
    - "cross topic argument mining from heterogeneous sources using attention based neural networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "argumentation mining in user generated web discourse"
    - "neural end to end learning for computational argumentation mining"
    - "what is the essence of a claim cross domain claim identification"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "argument mining for understanding peer reviews"):
  with members:
    - "deep contextualized word representations"
    - "a dataset of peer reviews peerread collection insights and nlp applications"
    - "argumentation mining in user generated web discourse"
    - "winning on the merits the joint effects of content and style on debate outcomes"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "argument mining with structured svms and rnns"
    - "parsing argumentation structures in persuasive essays"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-3 (induced by source paper "seeing things from a different angle discovering diverse perspectives about claims"):
  with members:
    - "a retrospective analysis of the fake news challenge stance detection task"
    - "fever a large scale dataset for fact extraction and verification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "twowingos a two wing optimization strategy for evidential claim verification"
    - "argumentation mining in user generated web discourse"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "parsing argumentation structures in persuasive essays"

Connected through hyperedge HE-4 (induced by source paper "fine grained argument unit recognition and classification"):
  with members:
    - "classification and clustering of arguments with contextualized word embeddings"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a bayesian approach for sequence tagging with crowds"
    - "argumentation mining in user generated web discourse"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-5 (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"
    - "social media argumentation mining the quest for deliberateness in raucousness"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-6 (induced by source paper "perspectroscope a window to the world of diverse perspectives"):
  with members:
    - "fakta an automatic end to end fact checking system"
    - "seeing things from a different angle discovering diverse perspectives about claims"
    - "fever a large scale dataset for fact extraction and verification"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-7 (induced by source paper "robust argument unit recognition and classification"):
  with members:
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "bayesian ensembles of crowds and deep learners for sequence tagging"
    - "neural end to end learning for computational argumentation mining"

Connected through hyperedge HE-8 (induced by source paper "neural argument generation augmented with externally retrieved evidence"):
  with members:
    - "neural end to end learning for computational argumentation mining"
    - "winning on the merits the joint effects of content and style on debate outcomes"
    - "argument mining with structured svms and rnns"
    - "sequence to sequence learning as beam search optimization"
    - "neural network based abstract generation for opinions and arguments"
    - "what to talk about and how selective generation using lstms with coarse to fine alignment"
    - "multi task sequence to sequence learning"
    - "adam a method for stochastic optimization"

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)