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 2015 task 3 answer selection in community question answering"
  Abstract: Community Question Answering (cQA) provides new interesting research directions to the traditional Question Answering (QA) field, e.g., the exploitation of the interaction between users and the structure of related posts. In this context, we organized SemEval-2015 Task 3 on "Answer Selection in cQA", which included two subtasks: (a) classifying answers as "good", "bad", or "potentially relevant" with respect to the question, and (b) answering a YES/NO question with "yes", "no", or "unsure", based on the list of all answers. We set subtask A for Arabic and English on two relatively different cQA domains, i.e., the Qatar Living website for English, and a Quran-related website for Arabic. We used crowdsourcing on Amazon Mechanical Turk to label a large English training dataset, which we released to the research community. Thirteen teams participated in the challenge with a total of 61 submissions: 24 primary and 37 contrastive. The best systems achieved an official score (macro-averaged F1) of 57.19 and 63.7 for the English subtasks A and B, and 78.55 for the Arabic subtask A.

Connected through hyperedge HE-1 (induced by source paper "training neural response selection for task oriented dialogue systems"):
  with members:
    - "passage re ranking with bert"
    - "simple applications of bert for ad hoc document retrieval"
    - "a repository of conversational datasets"
    - "multi task deep neural networks for natural language understanding"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"
    - "learning semantic textual similarity from conversations"
    - "improving response selection in multi turn dialogue systems by incorporating domain knowledge"
    - "retrieve and refine improved sequence generation models for dialogue"

Connected through hyperedge HE-2 (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 2016 task 3 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-3 (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 2016 task 3 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-4 (induced by source paper "semeval 2017 task 3 community question answering"):
  with members:
    - "hunting for troll comments in news community forums"
    - "semeval 2016 task 3 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-5 (induced by source paper "semeval 2016 task 3 community question answering"):
  with members:
    - "hunting for troll comments in news community forums"
    - "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"
    - "uh prhlt at semeval 2016 task 3 combining lexical and semantic based features for community question answering"
    - "answer sequence learning with neural networks for answer selection in community question answering"
    - "query chains learning to rank from implicit feedback"

Connected through hyperedge HE-6 (induced by source paper "semanticz at semeval 2016 task 3 ranking relevant answers in community question answering using semantic similarity based on fine tuned word embeddings"):
  with members:
    - "hunting for troll comments in news community forums"
    - "global thread level inference for comment classification in community question answering"
    - "pairwise neural machine translation evaluation"
    - "machine translation evaluation meets community question answering"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-7 (induced by source paper "global thread level inference for comment classification in community question answering"):
  with members:
    - "answer sequence learning with neural networks for answer selection in community question answering"
    - "a sentimental education sentiment analysis using subjectivity summarization based on minimum cuts"

Connected through hyperedge HE-8 (induced by source paper "an unsupervised model with attention autoencoders for question retrieval"):
  with members:
    - "semeval 2016 task 3 community question answering"
    - "semeval 2017 task 3 community question answering"
    - "uh prhlt at semeval 2016 task 3 combining lexical and semantic based features for community question answering"
    - "attention is all you need"
    - "a hierarchical neural autoencoder for paragraphs and documents"
    - "semi supervised sequence learning"
    - "adam a method for stochastic optimization"
    - "sequence to sequence learning with neural networks"

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)