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: "team papelo transformer networks at fever"
  Abstract: We develop a system for the FEVER fact extraction and verification challenge that uses a high precision entailment classifier based on transformer networks pretrained with language modeling, to classify a broad set of potential evidence. The precision of the entailment classifier allows us to enhance recall by considering every statement from several articles to decide upon each claim. We include not only the articles best matching the claim text by TFIDF score, but read additional articles whose titles match named entities and capitalized expressions occurring in the claim text. The entailment module evaluates potential evidence one statement at a time, together with the title of the page the evidence came from (providing a hint about possible pronoun antecedents). In preliminary evaluation, the system achieves .5736 FEVER score, .6108 label accuracy, and .6485 evidence F1 on the FEVER shared task test set.

Connected through hyperedge HE-1 (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"
    - "cognitive graph for multi hop reading comprehension at scale"
    - "evidence sentence extraction for machine reading comprehension"
    - "dynamically fused graph network for multi hop reasoning"
    - "combining fact extraction and verification with neural semantic matching networks"
    - "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-2 (induced by source paper "adversarial attacks against fact extraction and verification"):
  with members:
    - "combining fact extraction and verification with neural semantic matching networks"
    - "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-3 (induced by source paper "bert for evidence retrieval and claim verification"):
  with members:
    - "reasoning over semantic level graph for fact checking"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "combining fact extraction and verification with neural semantic matching networks"
    - "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"

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)