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: "character level intra attention network for natural language inference"
  Abstract: Natural language inference (NLI) is a central problem in language understanding. End-to-end artificial neural networks have reached state-of-the-art performance in NLI field recently. #R##N#In this paper, we propose Character-level Intra Attention Network (CIAN) for the NLI task. In our model, we use the character-level convolutional network to replace the standard word embedding layer, and we use the intra attention to capture the intra-sentence semantics. The proposed CIAN model provides improved results based on a newly published MNLI corpus.

Connected through hyperedge HE-1 (induced by source paper "distance based self attention network for natural language inference"):
  with members:
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"
    - "the repeval 2017 shared task multi genre natural language inference with sentence representations"
    - "shortcut stacked sentence encoders for multi domain inference"
    - "dynamic routing between capsules"
    - "refining raw sentence representations for textual entailment recognition via attention"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "recurrent neural network based sentence encoder with gated attention for natural language inference"

Connected through hyperedge HE-2 (induced by source paper "the repeval 2017 shared task multi genre natural language inference with sentence representations"):
  with members:
    - "supervised learning of universal sentence representations from natural language inference data"
    - "shortcut stacked sentence encoders for multi domain inference"
    - "refining raw sentence representations for textual entailment recognition via attention"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "recurrent neural network based sentence encoder with gated attention for natural language inference"
    - "enhanced lstm for natural language inference"
    - "character aware neural language models"
    - "learning natural language inference with lstm"

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)