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: "narrative modeling with memory chains and semantic supervision"
  Abstract: Story comprehension requires a deep semantic understanding of the narrative, making it a challenging task. Inspired by previous studies on ROC Story Cloze Test, we propose a novel method, tracking various semantic aspects with external neural memory chains while encouraging each to focus on a particular semantic aspect. Evaluated on the task of story ending prediction, our model demonstrates superior performance to a collection of competitive baselines, setting a new state of the art.

Connected through hyperedge HE-1 (induced by source paper "generating diverse story continuations with controllable semantics"):
  with members:
    - "writerforcing generating more interesting story endings"
    - "strategies for structuring story generation"
    - "unsupervised text style transfer using language models as discriminators"
    - "allennlp a deep semantic natural language processing platform"
    - "hierarchical neural story generation"
    - "texygen a benchmarking platform for text generation models"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"

Connected through hyperedge HE-2 (induced by source paper "demn distilled exposition enhanced matching network for story comprehension"):
  with members:
    - "multi turn inference matching network for natural language inference"
    - "a simple and effective approach to the story cloze test"
    - "the effect of different writing tasks on linguistic style a case study of the roc story cloze task"
    - "reading wikipedia to answer open domain questions"
    - "enhanced lstm for natural language inference"
    - "tracking the world state with recurrent entity networks"
    - "a compare aggregate model for matching text sequences"
    - "natural language inference by tree based convolution and heuristic matching"

Connected through hyperedge HE-3 (induced by source paper "evaluating the utility of hand crafted features in sequence labelling"):
  with members:
    - "deep contextualized word representations"
    - "recurrent entity networks with delayed memory update for targeted aspect based sentiment analysis"
    - "neural reranking for named entity recognition"
    - "improved neural machine translation with a syntax aware encoder and decoder"
    - "modeling source syntax for neural machine translation"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "neural architectures for named entity recognition"
    - "bidirectional lstm crf models for sequence tagging"

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)