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: "the fact extraction and verification fever shared task"
  Abstract: We present the results of the first Fact Extraction and VERification (FEVER) Shared Task. The task challenged participants to classify whether human-written factoid claims could be Supported or Refuted using evidence retrieved from Wikipedia. We received entries from 23 competing teams, 19 of which scored higher than the previously published baseline. The best performing system achieved a FEVER score of 64.21%. In this paper, we present the results of the shared task and a summary of the systems, highlighting commonalities and innovations among participating systems.

Connected through hyperedge HE-1 (induced by source paper "interpretable rumor detection in microblogs by attending to user interactions"):
  with members:
    - "combating fake news a survey on identification and mitigation techniques"
    - "self attention with relative position representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "attention is all you need"
    - "computational fact checking from knowledge networks"
    - "finding deceptive opinion spam by any stretch of the imagination"

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"
    - "combining fact extraction and verification with neural semantic matching networks"
    - "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"
    - "combining fact extraction and verification with neural semantic matching networks"
    - "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 "meta answering for machine reading"):
  with members:
    - "latent retrieval for weakly supervised open domain question answering"
    - "answering while summarizing multi task learning for multi hop qa with evidence extraction"
    - "synthetic qa corpora generation with roundtrip consistency"
    - "revealing the importance of semantic retrieval for machine reading at scale"
    - "a geometric perspective on optimal representations for reinforcement learning"
    - "a bert baseline for the natural questions"
    - "probing neural network comprehension of natural language arguments"
    - "episodic memory reader learning what to remember for question answering from streaming data"

Connected through hyperedge HE-6 (induced by source paper "danish stance classification and rumour resolution"):
  with members:
    - "fake news detection using stance classification a survey"
    - "but fit at semeval 2019 task 7 determining the rumour stance with pre trained deep bidirectional transformers"
    - "clearumor at semeval 2019 task 7 convolving elmo against rumors"
    - "a retrospective analysis of the fake news challenge stance detection task"
    - "detection and resolution of rumours in social media a survey"
    - "learning word vectors for 157 languages"
    - "rumoureval 2019 determining rumour veracity and support for rumours"
    - "semeval 2017 task 8 rumoureval determining rumour veracity and support for rumours"

Connected through hyperedge HE-7 (induced by source paper "the limitations of stylometry for detecting machine generated fake news"):
  with members:
    - "real or fake learning to discriminate machine from human generated text"
    - "gltr statistical detection and visualization of generated text"
    - "defending against neural fake news"
    - "the language of legal and illegal activity on the darknet"
    - "all in one multi task learning for rumour verification"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "fake news a survey of research detection methods and opportunities"
    - "fake news detection in social networks via crowd signals"

Connected through hyperedge HE-8 (induced by source paper "reasoning over semantic level graph for fact checking"):
  with members:
    - "towards debiasing fact verification models"
    - "adversarial attacks against fact extraction and verification"
    - "simple bert models for relation extraction and semantic role labeling"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "ukp athene multi sentence textual entailment for claim verification"
    - "deep contextualized word representations"
    - "fever a large scale dataset for fact extraction and verification"

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)