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: "sjtu nlp at semeval 2018 task 9 neural hypernym discovery with term embeddings"
  Abstract: This paper describes a hypernym discovery system for our participation in the SemEval-2018 Task 9, which aims to discover the best (set of) candidate hypernyms for input concepts or entities, given the search space of a pre-defined vocabulary. We introduce a neural network architecture for the concerned task and empirically study various neural network models to build the representations in latent space for words and phrases. The evaluated models include convolutional neural network, long-short term memory network, gated recurrent unit and recurrent convolutional neural network. We also explore different embedding methods, including word embedding and sense embedding for better performance.

Connected through hyperedge HE-1 (induced by source paper "effective subword segmentation for text comprehension"):
  with members:
    - "dependency or span end to end uniform semantic role labeling"
    - "discourse marker augmented network with reinforcement learning for natural language inference"
    - "dual co matching network for multi choice reading comprehension"
    - "i know what you want semantic learning for text comprehension"
    - "graph based bilingual word embedding for statistical machine translation"
    - "open vocabulary learning for neural chinese pinyin ime"
    - "deep contextualized word representations"
    - "subword augmented embedding for cloze reading comprehension"

Connected through hyperedge HE-2 (induced by source paper "subword augmented embedding for cloze reading comprehension"):
  with members:
    - "deep enhanced representation for implicit discourse relation recognition"
    - "deep contextualized word representations"
    - "one shot learning for question answering in gaokao history challenge"
    - "modeling multi turn conversation with deep utterance aggregation"
    - "character word lstm language models"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "fast and accurate neural word segmentation for chinese"
    - "the interplay of semantics and morphology in word embeddings"

Connected through hyperedge HE-3 (induced by source paper "one shot learning for question answering in gaokao history challenge"):
  with members:
    - "a question focused multi factor attention network for question answering"
    - "subword augmented embedding for cloze reading comprehension"
    - "modeling multi turn conversation with deep utterance aggregation"
    - "semi supervised qa with generative domain adaptive nets"
    - "bilateral multi perspective matching for natural language sentences"
    - "answering complex questions using open information extraction"
    - "question answering via integer programming over semi structured knowledge"
    - "one shot learning with memory augmented neural networks"

Connected through hyperedge HE-4 (induced by source paper "modeling multi turn conversation with deep utterance aggregation"):
  with members:
    - "deep enhanced representation for implicit discourse relation recognition"
    - "subword augmented embedding for cloze reading comprehension"
    - "one shot learning for question answering in gaokao history challenge"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "fast and accurate neural word segmentation for chinese"
    - "a network based end to end trainable task oriented dialogue system"

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)