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: "semanticz at semeval 2016 task 3 ranking relevant answers in community question answering using semantic similarity based on fine tuned word embeddings"
  Abstract: We describe our system for finding good answers in a community forum, as defined in SemEval-2016, Task 3 on Community Question Answering. Our approach relies on several semantic similarity features based on fine-tuned word embeddings and topics similarities. In the main Subtask C, our primary submission was ranked third, with a MAP of 51.68 and accuracy of 69.94. In Subtask A, our primary submission was also third, with MAP of 77.58 and accuracy of 73.39.

Connected through hyperedge HE-1 (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 2016 task 3 community question answering"
    - "semeval 2017 task 3 community question answering"
    - "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-2 (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"
    - "semeval 2016 task 3 community question answering"
    - "global thread level inference for comment classification in community question answering"
    - "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-3 (induced by source paper "semeval 2016 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"
    - "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-4 (induced by source paper "machine translation evaluation meets community question answering"):
  with members:
    - "semeval 2016 task 3 community question answering"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "can a suit of armor conduct electricity a new dataset for open book question answering"):
  with members:
    - "semeval 2016 task 3 community question answering"
    - "annotation artifacts in natural language inference data"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "worldtree a corpus of explanation graphs for elementary science questions supporting multi hop inference"
    - "tracking state changes in procedural text a challenge dataset and models for process paragraph comprehension"
    - "allennlp a deep semantic natural language processing platform"
    - "think you have solved question answering try arc the ai2 reasoning challenge"
    - "deep contextualized word representations"

Connected through hyperedge HE-6 (induced by source paper "joint multitask learning for community question answering using task specific embeddings"):
  with members:
    - "semeval 2016 task 3 community question answering"
    - "pairwise neural machine translation evaluation"
    - "adversarial domain adaptation for duplicate question detection"
    - "fact checking in community forums"
    - "large scale goodness polarity lexicons for community question answering"
    - "machine translation evaluation with neural networks"
    - "semi supervised question retrieval with gated convolutions"
    - "bidirectional lstm crf models for sequence tagging"

Connected through hyperedge HE-7 (induced by source paper "adversarial training for community question answer selection based on multi scale matching"):
  with members:
    - "semeval 2016 task 3 community question answering"
    - "uh prhlt at semeval 2016 task 3 combining lexical and semantic based features for community question answering"
    - "irgan a minimax game for unifying generative and discriminative information retrieval models"
    - "bilateral multi perspective matching for natural language sentences"
    - "deep reinforcement learning for dialogue generation"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "a decomposable attention model for natural language inference"
    - "a compare aggregate model for matching text sequences"

Connected through hyperedge HE-8 (induced by source paper "attentive recurrent tensor model for community question answering"):
  with members:
    - "learning to rank question answer pairs with holographic dual lstm architecture"
    - "sentence similarity learning by lexical decomposition and composition"
    - "neural variational inference for text processing"
    - "abcnn attention based convolutional neural network for modeling sentence pairs"
    - "deep learning for answer sentence 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)