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: "recurrent neural network encoder with attention for community question answering"
  Abstract: We apply a general recurrent neural network (RNN) encoder framework to community question answering (cQA) tasks. Our approach does not rely on any linguistic processing, and can be applied to different languages or domains. Further improvements are observed when we extend the RNN encoders with a neural attention mechanism that encourages reasoning over entire sequences. To deal with practical issues such as data sparsity and imbalanced labels, we apply various techniques such as transfer learning and multitask learning. Our experiments on the SemEval-2016 cQA task show 10% improvement on a MAP score compared to an information retrieval-based approach, and achieve comparable performance to a strong handcrafted feature-based method.

Connected through hyperedge HE-1 (induced by source paper "no need to pay attention simple recurrent neural networks work for answering simple questions"):
  with members:
    - "simple question answering by attentive convolutional neural network"
    - "character level question answering with attention"
    - "bidirectional lstm crf models for sequence tagging"
    - "part of speech tagging with bidirectional long short term memory recurrent neural network"
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "large scale simple question answering with memory networks"
    - "ask me anything dynamic memory networks for natural language processing"
    - "on the properties of neural machine translation encoder decoder approaches"

Connected through hyperedge HE-2 (induced by source paper "neural attention models for sequence classification analysis and application to key term extraction and dialogue act detection"):
  with members:
    - "the influence of context on dialogue act recognition"
    - "show attend and tell neural image caption generation with visual attention"
    - "ask attend and answer exploring question guided spatial attention for visual question answering"
    - "neural machine translation by jointly learning to align and translate"
    - "a new approach to keyphrase extraction using neural networks"

Connected through hyperedge HE-3 (induced by source paper "deep feature fusion network for answer quality prediction in community question answering"):
  with members:
    - "semeval 2015 task 3 answer selection in community question answering"
    - "empirical evaluation of rectified activations in convolutional network"
    - "deep learning for answer sentence selection"
    - "distributed representations of sentences and documents"
    - "tagme on the fly annotation of short text fragments by wikipedia entities"

Connected through hyperedge HE-4 (induced by source paper "simple and effective question answering with recurrent neural networks"):
  with members:
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "large scale simple question answering with memory networks"
    - "ask me anything dynamic memory networks for natural language processing"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"

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)