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: "modelling interaction of sentence pair with coupled lstms"
  Abstract: Recently, there is rising interest in modelling the interactions of two sentences with deep neural networks. However, most of the existing methods encode two sequences with separate encoders, in which a sentence is encoded with little or no information from the other sentence. In this paper, we propose a deep architecture to model the strong interaction of sentence pair with two coupled-LSTMs. Specifically, we introduce two coupled ways to model the interdependences of two LSTMs, coupling the local contextualized interactions of two sentences. We then aggregate these interactions and use a dynamic pooling to select the most informative features. Experiments on two very large datasets demonstrate the efficacy of our proposed architecture and its superiority to state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "what if we simply swap the two text fragments a straightforward yet effective way to test the robustness of methods to confounding signals in nature language inference tasks"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "hypothesis only baselines in natural language inference"
    - "deep contextualized word representations"
    - "adversarially regularising neural nli models to integrate logical background knowledge"
    - "semantic sentence matching with densely connected recurrent and co attentive information"
    - "variable generalization performance of a deep learning model to detect pneumonia in chest radiographs a cross sectional study"
    - "enhancing sentence embedding with generalized pooling"

Connected through hyperedge HE-2 (induced by source paper "multi turn inference matching network for natural language inference"):
  with members:
    - "dr bilstm dependent reading bidirectional lstm for natural language inference"
    - "natural language inference over interaction space"
    - "natural language inference from multiple premises"
    - "a compare propagate architecture with alignment factorization for natural language inference"
    - "learned in translation contextualized word vectors"
    - "shortcut stacked sentence encoders for multi domain inference"
    - "compare compress and propagate enhancing neural architectures with alignment factorization for natural language inference"
    - "bilateral multi perspective matching for natural language sentences"

Connected through hyperedge HE-3 (induced by source paper "a sequential matching framework for multi turn response selection in retrieval based chatbots"):
  with members:
    - "learning matching models with weak supervision for response selection in retrieval based chatbots"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "adversarial learning for neural dialogue generation"
    - "a persona based neural conversation model"
    - "deep reinforcement learning for dialogue generation"
    - "sequence to backward and forward sequences a content introducing approach to generative short text conversation"
    - "a decomposable attention model for natural language inference"
    - "multiresolution recurrent neural networks an application to dialogue response generation"

Connected through hyperedge HE-4 (induced by source paper "bilateral multi perspective matching for natural language sentences"):
  with members:
    - "attentive pooling networks"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "semi supervised clustering for short text via deep representation learning"
    - "long short term memory networks for machine reading"
    - "a decomposable attention model for natural language inference"
    - "sentence similarity learning by lexical decomposition and composition"
    - "enhanced lstm for natural language inference"
    - "learning natural language inference using bidirectional lstm model and inner attention"

Connected through hyperedge HE-5 (induced by source paper "knowledge enhanced hybrid neural network for text matching"):
  with members:
    - "incorporating loose structured knowledge into conversation modeling via recall gate lstm"
    - "theano a python framework for fast computation of mathematical expressions"
    - "text matching as image recognition"
    - "match srnn modeling the recursive matching structure with spatial rnn"
    - "abcnn attention based convolutional neural network for modeling sentence pairs"
    - "the ubuntu dialogue corpus a large dataset for research in unstructured multi turn dialogue systems"
    - "convolutional neural network architectures for matching natural language sentences"
    - "a deep architecture for semantic matching with multiple positional sentence representations"

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)