human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "attentive recurrent tensor model for community question answering"):
  Abstract: A major challenge to the problem of community question answering is the lexical and semantic gap between the sentence representations. Some solutions to minimize this gap includes the introduction of extra parameters to deep models or augmenting the external handcrafted features. In this paper, we propose a novel attentive recurrent tensor network for solving the lexical and semantic gap in community question answering. We introduce token-level and phrase-level attention strategy that maps input sequences to the output using trainable parameters. Further, we use the tensor parameters to introduce a 3-way interaction between question, answer and external features in vector space. We introduce simplified tensor matrices with L2 regularization that results in smooth optimization during training. The proposed model achieves state-of-the-art performance on the task of answer sentence selection (TrecQA and WikiQA datasets) while outperforming the current state-of-the-art on the tasks of best answer selection (Yahoo! L4) and answer triggering task (WikiQA).

Includes members:
  Member "semanticz at semeval 2016 task 3 ranking relevant answers in community question answering using semantic similarity based on fine tuned word embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "automatic fact checking using context and discourse information")
  Member "learning to rank question answer pairs with holographic dual lstm architecture":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "text based question answering from information retrieval and deep neural network perspectives a survey")
  Member "sentence similarity learning by lexical decomposition and composition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "banditrank learning to rank using contextual bandits")
  Member "neural variational inference for text processing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "text based question answering from information retrieval and deep neural network perspectives a survey")
  Member "abcnn attention based convolutional neural network for modeling sentence pairs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "rl duet online music accompaniment generation using deep reinforcement learning")
  Member "deep learning for answer sentence selection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a study on efficiency accuracy and document structure for answer sentence selection")

Question: Please tell me which class the source paper 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)