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: "towards automated factchecking developing an annotation schema and benchmark for consistent automated claim detection"
  Abstract: In an effort to assist factcheckers in the process of factchecking, we tackle the claim detection task, one of the necessary stages prior to determining the veracity of a claim. It consists of identifying the set of sentences, out of a long text, deemed capable of being factchecked. This paper is a collaborative work between Full Fact, an independent factchecking charity, and academic partners. Leveraging the expertise of professional factcheckers, we develop an annotation schema and a benchmark for automated claim detection that is more consistent across time, topics and annotators than previous approaches. Our annotation schema has been used to crowdsource the annotation of a dataset with sentences from UK political TV shows. We introduce an approach based on universal sentence representations to perform the classification, achieving an F1 score of 0.83, with over 5% relative improvement over the state-of-the-art methods ClaimBuster and ClaimRank. The system was deployed in production and received positive user feedback.

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"
    - "claimrank detecting check worthy claims in arabic and english"
    - "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"
    - "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"
    - "claimrank detecting check worthy claims in arabic and english"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "citation needed a taxonomy and algorithmic assessment of wikipedia s verifiability"):
  with members:
    - "fever a large scale dataset for fact extraction and verification"
    - "fine grained citation span for references in wikipedia"
    - "enriching word vectors with subword information"
    - "bag of tricks for efficient text classification"
    - "finding news citations for wikipedia"
    - "automated news suggestions for populating wikipedia entity pages"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-4 (induced by source paper "the evolution of argumentation mining from models to social media and emerging tools"):
  with members:
    - "claudette an automated detector of potentially unfair clauses in online terms of service"
    - "argumentation mining exploiting multiple sources and background knowledge"
    - "multi task learning for argumentation mining in low resource settings"
    - "cross topic argument mining from heterogeneous sources using attention based neural networks"
    - "argumentation mining in user generated web discourse"
    - "the argument reasoning comprehension task identification and reconstruction of implicit warrants"
    - "mitre at semeval 2016 task 6 transfer learning for stance detection"
    - "stance and sentiment in tweets"

Connected through hyperedge HE-5 (induced by source paper "neural check worthiness ranking with weak supervision finding sentences for fact checking"):
  with members:
    - "claimrank detecting check worthy claims in arabic and english"
    - "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"
    - "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-6 (induced by source paper "combining similarity features and deep representation learning for stance detection in the context of checking fake news"):
  with members:
    - "discourse marker augmented network with reinforcement learning for natural language inference"
    - "universal transformers"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "automatic stance detection using end to end memory networks"
    - "co stack residual affinity networks with multi level attention refinement for matching text sequences"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "debunking fake news one feature at a time"

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)