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: "combining fact extraction and verification with neural semantic matching networks"
  Abstract: The increasing concern with misinformation has stimulated research efforts on automatic fact checking. The recently-released FEVER dataset introduced a benchmark fact-verification task in which a system is asked to verify a claim using evidential sentences from Wikipedia documents. In this paper, we present a connected system consisting of three homogeneous neural semantic matching models that conduct document retrieval, sentence selection, and claim verification jointly for fact extraction and verification. For evidence retrieval (document retrieval and sentence selection), unlike traditional vector space IR models in which queries and sources are matched in some pre-designed term vector space, we develop neural models to perform deep semantic matching from raw textual input, assuming no intermediate term representation and no access to structured external knowledge bases. We also show that Pageview frequency can also help improve the performance of evidence retrieval results, that later can be matched by using our neural semantic matching network. For claim verification, unlike previous approaches that simply feed upstream retrieved evidence and the claim to a natural language inference (NLI) model, we further enhance the NLI model by providing it with internal semantic relatedness scores (hence integrating it with the evidence retrieval modules) and ontological WordNet features. Experiments on the FEVER dataset indicate that (1) our neural semantic matching method outperforms popular TF-IDF and encoder models, by significant margins on all evidence retrieval metrics, (2) the additional relatedness score and WordNet features improve the NLI model via better semantic awareness, and (3) by formalizing all three subtasks as a similar semantic matching problem and improving on all three stages, the complete model is able to achieve the state-of-the-art results on the FEVER test set.

Connected through hyperedge HE-1 (induced by source paper "adversarial nli a new benchmark for natural language understanding"):
  with members:
    - "adversarial filters of dataset biases"
    - "hellaswag can a machine really finish your sentence"
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "abductive commonsense reasoning"
    - "codah an adversarially authored question answer dataset for common sense"
    - "do neural dialog systems use the conversation history effectively an empirical study"
    - "multi task deep neural networks for natural language understanding"
    - "are we modeling the task or the annotator an investigation of annotator bias in natural language understanding datasets"

Connected through hyperedge HE-2 (induced by source paper "answering while summarizing multi task learning for multi hop qa with evidence extraction"):
  with members:
    - "coarse grain fine grain coattention network for multi evidence question answering"
    - "team papelo transformer networks at fever"
    - "cognitive graph for multi hop reading comprehension at scale"
    - "evidence sentence extraction for machine reading comprehension"
    - "dynamically fused graph network for multi hop reasoning"
    - "the fact extraction and verification fever shared task"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "ukp athene multi sentence textual entailment for claim verification"

Connected through hyperedge HE-3 (induced by source paper "kernel graph attention network for fact verification"):
  with members:
    - "several experiments on investigating pretraining and knowledge enhanced models for natural language inference"
    - "bert for evidence retrieval and claim verification"
    - "reasoning over semantic level graph for fact checking"
    - "gear graph based evidence aggregating and reasoning for fact verification"
    - "understanding attention and generalization in graph neural networks"
    - "deeper text understanding for ir with contextual neural language modeling"
    - "cedr contextualized embeddings for document ranking"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-4 (induced by source paper "adversarial attacks against fact extraction and verification"):
  with members:
    - "team papelo transformer networks at fever"
    - "the fact extraction and verification fever shared task"
    - "automated fact checking task formulations methods and future directions"
    - "ukp athene multi sentence textual entailment for claim verification"
    - "hypothesis only baselines in natural language inference"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "fever a large scale dataset for fact extraction and verification"

Connected through hyperedge HE-5 (induced by source paper "towards debiasing fact verification models"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "identifying and correcting label bias in machine learning"
    - "probing neural network comprehension of natural language arguments"
    - "annotation artifacts in natural language inference data"
    - "hypothesis only baselines in natural language inference"
    - "know what you don t know unanswerable questions for squad"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"

Connected through hyperedge HE-6 (induced by source paper "revealing the importance of semantic retrieval for machine reading at scale"):
  with members:
    - "cognitive graph for multi hop reading comprehension at scale"
    - "ranking paragraphs for improving answer recall in open domain question answering"
    - "automated fact checking task formulations methods and future directions"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "ukp athene multi sentence textual entailment for claim verification"
    - "deep contextualized word representations"
    - "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 "bert for evidence retrieval and claim verification"):
  with members:
    - "reasoning over semantic level graph for fact checking"
    - "team papelo transformer networks at fever"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "fever a large scale dataset for fact extraction and verification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "enhancing and combining sequential and tree lstm for natural language inference"

Connected through hyperedge HE-8 (induced by source paper "unsupervised question answering for fact checking"):
  with members:
    - "automated fact checking task formulations methods and future directions"
    - "ukp athene multi sentence textual entailment for claim verification"
    - "improving neural question generation using answer separation"
    - "fever a large scale dataset for fact extraction and verification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning to ask neural question generation for reading comprehension"
    - "bilateral multi perspective matching for natural language sentences"
    - "generating factoid questions with recurrent neural networks the 30m factoid question answer corpus"

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)