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: "ukp athene multi sentence textual entailment for claim verification"
  Abstract: The Fact Extraction and VERification (FEVER) shared task was launched to support the development of systems able to verify claims by extracting supporting or refuting facts from raw text. The shared task organizers provide a large-scale dataset for the consecutive steps involved in claim verification, in particular, document retrieval, fact extraction, and claim classification. In this paper, we present our claim verification pipeline approach, which, according to the preliminary results, scored third in the shared task, out of 23 competing systems. For the document retrieval, we implemented a new entity linking approach. In order to be able to rank candidate facts and classify a claim on the basis of several selected facts, we introduce two extensions to the Enhanced LSTM (ESIM).

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"
    - "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"
    - "the fact extraction and verification fever shared task"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"

Connected through hyperedge HE-2 (induced by source paper "evidence sentence extraction for machine reading comprehension"):
  with members:
    - "dream a challenge dataset and models for dialogue based reading comprehension"
    - "training a ranking function for open domain question answering"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "neural models for reasoning over multiple mentions using coreference"
    - "know what you don t know unanswerable questions for squad"
    - "coqa a conversational question answering challenge"
    - "deep probabilistic logic a unifying framework for indirect supervision"
    - "fever a large scale dataset for fact extraction and verification"

Connected through hyperedge HE-3 (induced by source paper "how decoding strategies affect the verifiability of generated text"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "language models as knowledge bases"
    - "investigating entity knowledge in bert with simple neural end to end entity linking"
    - "mixture content selection for diverse sequence generation"
    - "barack s wife hillary using knowledge graphs for fact aware language modeling"
    - "the curious case of neural text degeneration"
    - "tmlab generative enhanced model gem for adversarial attacks"
    - "real or fake learning to discriminate machine from human generated text"

Connected through hyperedge HE-4 (induced by source paper "tabfact a large scale dataset for table based fact verification"):
  with members:
    - "learning to generalize from sparse and underspecified rewards"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "superglue a stickier benchmark for general purpose language understanding systems"
    - "roberta a robustly optimized bert pretraining approach"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "a corpus for reasoning about natural language grounded in photographs"
    - "deep contextualized word representations"

Connected through hyperedge HE-5 (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-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"
    - "automated fact checking task formulations methods and future directions"
    - "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 "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"
    - "automated fact checking task formulations methods and future directions"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "deep contextualized word representations"
    - "fever a large scale dataset for fact extraction and verification"
    - "bert pre training of deep bidirectional transformers for language understanding"

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"
    - "the fact extraction and verification fever shared task"
    - "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.IR(Information Retrieval)