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: "semeval 2016 task 3 community question answering"
  Abstract: This paper describes the SemEval--2016 Task 3 on Community Question Answering, which we offered in English and Arabic. For English, we had three subtasks: Question--Comment Similarity (subtask A), Question--Question Similarity (B), and Question--External Comment Similarity (C). For Arabic, we had another subtask: Rerank the correct answers for a new question (D). Eighteen teams participated in the task, submitting a total of 95 runs (38 primary and 57 contrastive) for the four subtasks. A variety of approaches and features were used by the participating systems to address the different subtasks, which are summarized in this paper. The best systems achieved an official score (MAP) of 79.19, 76.70, 55.41, and 45.83 in subtasks A, B, C, and D, respectively. These scores are significantly better than those for the baselines that we provided. For subtask A, the best system improved over the 2015 winner by 3 points absolute in terms of Accuracy.

Connected through hyperedge HE-1 (induced by source paper "review guided helpful answer identification in e commerce"):
  with members:
    - "semeval 2017 task 3 community question answering"
    - "joint learning of answer selection and answer summary generation in community question answering"
    - "learning semantic textual similarity from conversations"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning to rank question answer pairs with holographic dual lstm architecture"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "attentive pooling networks"

Connected through hyperedge HE-2 (induced by source paper "checkthat at clef 2020 enabling the automatic identification and verification of claims in social media"):
  with members:
    - "it takes nine to smell a rat neural multi task learning for check worthiness prediction"
    - "overview of the clef 2018 checkthat lab on automatic identification and verification of political claims task 1 check worthiness"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"
    - "fake news detection on social media a data mining perspective"
    - "fully automated fact checking using external sources"
    - "analysing how people orient to and spread rumours in social media by looking at conversational threads"
    - "leveraging joint interactions for credibility analysis in news communities"
    - "tweetcred real time credibility assessment of content on twitter"

Connected through hyperedge HE-3 (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 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"
    - "claimrank detecting check worthy claims in arabic and english"
    - "automatic stance detection using end to end memory networks"
    - "integrating stance detection and fact checking in a unified corpus"

Connected through hyperedge HE-4 (induced by source paper "semeval 2019 task 8 fact checking in community question answering forums"):
  with members:
    - "hunting for troll comments in news community forums"
    - "in search of credible news"
    - "tmlab srpol at semeval 2019 task 8 fact checking in community question answering forums"
    - "semeval 2015 task 3 answer selection in community question answering"
    - "multi task ordinal regression for jointly predicting the trustworthiness and the leading political ideology of news media"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "predicting factuality of reporting and bias of news media sources"
    - "universal sentence encoder"

Connected through hyperedge HE-5 (induced by source paper "a question entailment approach to question answering"):
  with members:
    - "semeval 2017 task 3 community question answering"
    - "semantic sentence matching with densely connected recurrent and co attentive information"
    - "dr bilstm dependent reading bidirectional lstm for natural language inference"
    - "neural natural language inference models enhanced with external knowledge"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "cmu livemedqa at trec 2017 liveqa a consumer health question answering system"
    - "neural question answering at bioasq 5b"
    - "the repeval 2017 shared task multi genre natural language inference with sentence representations"

Connected through hyperedge HE-6 (induced by source paper "semeval 2017 task 3 community question answering"):
  with members:
    - "hunting for troll comments in news community forums"
    - "semeval 2015 task 3 answer selection in community question answering"
    - "global thread level inference for comment classification in 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"
    - "pairwise neural machine translation evaluation"
    - "machine translation evaluation meets community question answering"
    - "a decomposable attention model for natural language inference"
    - "semi supervised question retrieval with gated convolutions"

Connected through hyperedge HE-7 (induced by source paper "bertsel answer selection with pre trained models"):
  with members:
    - "semeval 2017 task 3 community question answering"
    - "hyperbolic representation learning for fast and efficient neural question answering"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a strong baseline for question relevancy ranking"
    - "learning to rank question answer pairs with holographic dual lstm architecture"

Connected through hyperedge HE-8 (induced by source paper "machine translation evaluation meets community question answering"):
  with members:
    - "semanticz at semeval 2016 task 3 ranking relevant answers in community question answering using semantic similarity based on fine tuned word embeddings"
    - "pairwise neural machine translation evaluation"
    - "semi supervised question retrieval with gated convolutions"
    - "applying deep learning to answer selection a study and an open task"
    - "word embedding based correlation model for question answer matching"
    - "lstm based deep learning models for non factoid answer selection"

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)