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: "what is the essence of a claim cross domain claim identification"
  Abstract: Argument mining has become a popular research area in NLP. It typically includes the identification of argumentative components, e.g. claims, as the central component of an argument. We perform a qualitative analysis across six different datasets and show that these appear to conceptualize claims quite differently. To learn about the consequences of such different conceptualizations of claim for practical applications, we carried out extensive experiments using state-of-the-art feature-rich and deep learning systems, to identify claims in a cross-domain fashion. While the divergent perception of claims in different datasets is indeed harmful to cross-domain classification, we show that there are shared properties on the lexical level as well as system configurations that can help to overcome these gaps.

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"
    - "attention is all you need"
    - "understanding and detecting supporting arguments of diverse types"

Connected through hyperedge HE-2 (induced by source paper "imho fine tuning improves claim detection"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "glomo unsupervisedly learned relational graphs as transferable representations"
    - "argumentation mining in user generated web discourse"
    - "neural end to end learning for computational argumentation mining"
    - "parsing argumentation structures in persuasive essays"

Connected through hyperedge HE-3 (induced by source paper "neural symbolic argumentation mining an argument in favour of deep learning and reasoning"):
  with members:
    - "deep learning for sentiment analysis a survey"
    - "argument component classification for classroom discussions"
    - "multi task learning for argumentation mining in low resource settings"
    - "deep residual learning for weakly supervised relation extraction"
    - "argument mining with structured svms and rnns"
    - "logic tensor networks deep learning and logical reasoning from data and knowledge"
    - "defeasible logic programming an argumentative approach"

Connected through hyperedge HE-4 (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"
    - "social media argumentation mining the quest for deliberateness in raucousness"
    - "understanding and detecting supporting arguments of diverse types"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-5 (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"
    - "social media argumentation mining the quest for deliberateness in raucousness"
    - "xgboost a scalable tree boosting system"
    - "learning distributed representations of sentences from unlabelled data"

Connected through hyperedge HE-6 (induced by source paper "argument component classification for classroom discussions"):
  with members:
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "argumentation mining in user generated web discourse"
    - "predicting specificity in classroom discussion"
    - "argument mining with structured svms and rnns"
    - "learning to learn by gradient descent by gradient descent"
    - "parsing argumentation structures in persuasive essays"
    - "convolutional neural networks for sentence classification"
    - "practical recommendations for gradient based training of deep architectures"

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)