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: "a co matching model for multi choice reading comprehension"
  Abstract: Multi-choice reading comprehension is a challenging task, which involves the matching between a passage and a question-answer pair. This paper proposes a new co-matching approach to this problem, which jointly models whether a passage can match both a question and a candidate answer. Experimental results on the RACE dataset demonstrate that our approach achieves state-of-the-art performance.

Connected through hyperedge HE-1 (induced by source paper "gennet reading comprehension with multiple choice questions using generation and selection model"):
  with members:
    - "eliminet a model for eliminating options for reading comprehension with multiple choice questions"
    - "convolutional spatial attention model for reading comprehension with multiple choice questions"
    - "option comparison network for multiple choice reading comprehension"
    - "dual co matching network for multi choice reading comprehension"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "race large scale reading comprehension dataset from examinations"
    - "s net from answer extraction to answer generation for machine reading comprehension"
    - "a thorough examination of the cnn daily mail reading comprehension task"

Connected through hyperedge HE-2 (induced by source paper "do multi hop readers dream of reasoning chains"):
  with members:
    - "coarse grain fine grain coattention network for multi evidence question answering"
    - "compositional questions do not necessitate multi hop reasoning"
    - "dual co matching network for multi choice reading comprehension"
    - "repartitioning of the complexwebquestions dataset"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "question answering by reasoning across documents with graph convolutional networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "exploring graph structured passage representation for multi hop reading comprehension with graph neural networks"

Connected through hyperedge HE-3 (induced by source paper "asymmetrical hierarchical networks with attentive interactions for interpretable review based recommendation"):
  with members:
    - "reinforcement knowledge graph reasoning for explainable recommendation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "attention based deep multiple instance learning"
    - "deep contextualized word representations"
    - "multi pointer co attention networks for recommendation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "transnets learning to transform for recommendation"

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)