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: "claimrank detecting check worthy claims in arabic and english"
  Abstract: We present ClaimRank, an online system for detecting check-worthy claims. While originally trained on political debates, the system can work for any kind of text, e.g., interviews or regular news articles. Its aim is to facilitate manual fact-checking efforts by prioritizing the claims that fact-checkers should consider first. ClaimRank supports both Arabic and English, it is trained on actual annotations from nine reputable fact-checking organizations (PolitiFact, FactCheck, ABC, CNN, NPR, NYT, Chicago Tribune, The Guardian, and Washington Post), and thus it can mimic the claim selection strategies for each and any of them, as well as for the union of them all.

Connected through hyperedge HE-1 (induced by source paper "gradient based adversarial training on transformer networks for detecting check worthy factual claims"):
  with members:
    - "fakta an automatic end to end fact checking system"
    - "how to fine tune bert for text classification"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "towards automated factchecking developing an annotation schema and benchmark for consistent automated claim detection"
    - "supervised learning of universal sentence representations from natural language inference data"

Connected through hyperedge HE-2 (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"
    - "automated fact checking task formulations methods and future directions"
    - "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-3 (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"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "automated fact checking task formulations methods and future directions"
    - "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-4 (induced by source paper "predicting the role of political trolls in social media"):
  with members:
    - "hunting for troll comments in news community forums"
    - "in search of credible news"
    - "semeval 2019 task 8 fact checking in community question answering forums"
    - "political discourse on social media echo chambers gatekeepers and the price of bipartisanship"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "fact checking in community forums"
    - "an army of me sockpuppets in online discussion communities"
    - "the effect of collective attention on controversial debates on social media"

Connected through hyperedge HE-5 (induced by source paper "automatic fact checking using context and discourse information"):
  with members:
    - "hunting for troll comments in news community forums"
    - "in search of credible news"
    - "semeval 2015 task 3 answer selection in community question answering"
    - "semeval 2016 task 3 community question answering"
    - "semeval 2017 task 3 community question answering"
    - "semanticz at semeval 2016 task 3 ranking relevant answers in community question answering using semantic similarity based on fine tuned word embeddings"
    - "automatic stance detection using end to end memory networks"
    - "integrating stance detection and fact checking in a unified corpus"

Connected through hyperedge HE-6 (induced by source paper "neural check worthiness ranking with weak supervision finding sentences for fact checking"):
  with members:
    - "visual interpretability for deep learning a survey"
    - "automated fact checking task formulations methods and future directions"
    - "detection and resolution of rumours in social media a survey"
    - "towards automated factchecking developing an annotation schema and benchmark for consistent automated claim detection"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "fully automated fact checking using external sources"
    - "computational fact checking from knowledge networks"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "explainable fact checking with probabilistic answer set programming"):
  with members:
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "fever a large scale dataset for fact extraction and verification"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "finding streams in knowledge graphs to support fact checking"
    - "computing lpmln using asp and mln solvers"
    - "discriminative predicate path mining for fact checking in knowledge graphs"
    - "computational fact checking from knowledge networks"

Connected through hyperedge HE-8 (induced by source paper "real time claim detection from news articles and retrieval of semantically similar factchecks"):
  with members:
    - "automated fact checking task formulations methods and future directions"
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "a large annotated corpus for learning natural language inference"
    - "efficient estimation of word representations in vector space"

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)