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: "automated fact checking task formulations methods and future directions"
  Abstract: The recently increased focus on misinformation has stimulated research in fact checking, the task of assessing the truthfulness of a claim. Research in automating this task has been conducted in a variety of disciplines including natural language processing, machine learning, knowledge representation, databases, and journalism. While there has been substantial progress, relevant papers and articles have been published in research communities that are often unaware of each other and use inconsistent terminology, thus impeding understanding and further progress. In this paper we survey automated fact checking research stemming from natural language processing and related disciplines, unifying the task formulations and methodologies across papers and authors. Furthermore, we highlight the use of evidence as an important distinguishing factor among them cutting across task formulations and methods. We conclude with proposing avenues for future NLP research on automated fact checking.

Connected through hyperedge HE-1 (induced by source paper "fact check worthiness detection as positive unlabelled learning"):
  with members:
    - "citation needed a taxonomy and algorithmic assessment of wikipedia s verifiability"
    - "distantly supervised named entity recognition using positive unlabeled learning"
    - "claimrank detecting check worthy claims in arabic and english"
    - "learning from positive and unlabeled data a survey"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "towards automated factchecking developing an annotation schema and benchmark for consistent automated claim detection"

Connected through hyperedge HE-2 (induced by source paper "scrutinizer a mixed initiative approach to large scale data driven claim verification"):
  with members:
    - "explainable fact checking with probabilistic answer set programming"
    - "claimrank detecting check worthy claims in arabic and english"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "fact checking in community forums"
    - "discriminative predicate path mining for fact checking in knowledge graphs"
    - "the rise of social bots"
    - "computational fact checking from knowledge networks"
    - "believe it or not adding belief annotations to databases"

Connected through hyperedge HE-3 (induced by source paper "information credibility in the social web contexts approaches and open issues"):
  with members:
    - "on the inevitability of online echo chambers"
    - "combating fake news a survey on identification and mitigation techniques"
    - "the spread of low credibility content by social bots"
    - "automated hate speech detection and the problem of offensive language"
    - "fake news detection on social media a data mining perspective"
    - "echo chambers in the age of misinformation"

Connected through hyperedge HE-4 (induced by source paper "detecting toxicity in news articles application to bulgarian"):
  with members:
    - "hunting for troll comments in news community forums"
    - "in search of credible news"
    - "multi task ordinal regression for jointly predicting the trustworthiness and the leading political ideology of news media"
    - "predicting the leading political ideology of youtube channels using acoustic textual and metadata information"
    - "fact checking meets fauxtography verifying claims about images"
    - "fine grained analysis of propaganda in news articles"
    - "a retrospective analysis of the fake news challenge stance detection task"
    - "all in one multi task learning for rumour verification"

Connected through hyperedge HE-5 (induced by source paper "false news on social media a data driven survey"):
  with members:
    - "the junk news aggregator examining junk news posted on facebook starting with the 2018 us midterm elections"
    - "a retrospective analysis of the fake news challenge stance detection task"
    - "the rise of guardians fact checking url recommendation to combat fake news"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "anatomy of an online misinformation network"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "detection and resolution of rumours in social media a survey"
    - "the spread of low credibility content by social bots"

Connected through hyperedge HE-6 (induced by source paper "adversarial attacks against fact extraction and verification"):
  with members:
    - "team papelo transformer networks at fever"
    - "combining fact extraction and verification with neural semantic matching networks"
    - "the fact extraction and verification fever shared task"
    - "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-7 (induced by source paper "rc qed evaluating natural language derivations in multi hop reading comprehension"):
  with members:
    - "coarse grain fine grain coattention network for multi evidence question answering"
    - "explain yourself leveraging language models for commonsense reasoning"
    - "worldtree a corpus of explanation graphs for elementary science questions supporting multi hop inference"
    - "neural models for reasoning over multiple mentions using coreference"
    - "multi hop inference for sentence level textgraphs how challenging is meaningfully combining information for science question answering"
    - "e snli natural language inference with natural language explanations"
    - "neural automated essay scoring and coherence modeling for adversarially crafted input"
    - "question answering by reasoning across documents with graph convolutional networks"

Connected through hyperedge HE-8 (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"
    - "combining fact extraction and verification with neural semantic matching networks"
    - "ranking paragraphs for improving answer recall in open domain question answering"
    - "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"

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)