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: "lingke a fine grained multi turn chatbot for customer service"
  Abstract: Traditional chatbots usually need a mass of human dialogue data, especially when using supervised machine learning method. Though they can easily deal with single-turn question answering, for multi-turn the performance is usually unsatisfactory. In this paper, we present Lingke, an information retrieval augmented chatbot which is able to answer questions based on given product introduction document and deal with multi-turn conversations. We will introduce a fine-grained pipeline processing to distill responses based on unstructured documents, and attentive sequential context-response matching for multi-turn conversations.

Connected through hyperedge HE-1 (induced by source paper "a neural entity coreference resolution review"):
  with members:
    - "context in neural machine translation a review of models and evaluations"
    - "incorporating context and external knowledge for pronoun coreference resolution"
    - "gendered ambiguous pronouns shared task boosting model confidence by evidence pooling"
    - "knowledge aware pronoun coreference resolution"
    - "measuring bias in contextualized word representations"
    - "improving generalization in coreference resolution via adversarial training"
    - "look again at the syntax relational graph convolutional network for gendered ambiguous pronoun resolution"
    - "gender bias in contextualized word embeddings"

Connected through hyperedge HE-2 (induced by source paper "effective character augmented word embedding for machine reading comprehension"):
  with members:
    - "deep enhanced representation for implicit discourse relation recognition"
    - "deep contextualized word representations"
    - "subword augmented embedding for cloze reading comprehension"
    - "one shot learning for question answering in gaokao history challenge"
    - "multi passage machine reading comprehension with cross passage answer verification"
    - "modeling multi turn conversation with deep utterance aggregation"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "attention over attention neural networks for reading comprehension"

Connected through hyperedge HE-3 (induced by source paper "i know what you want semantic learning for text comprehension"):
  with members:
    - "discourse marker augmented network with reinforcement learning for natural language inference"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "know what you don t know unanswerable questions for squad"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "deep contextualized word representations"
    - "subword augmented embedding for cloze reading comprehension"
    - "one shot learning for question answering in gaokao history challenge"
    - "large scale qa srl parsing"

Connected through hyperedge HE-4 (induced by source paper "open vocabulary learning for neural chinese pinyin ime"):
  with members:
    - "dependency or span end to end uniform semantic role labeling"
    - "lattice based transformer encoder for neural machine translation"
    - "head driven phrase structure grammar parsing on penn treebank"
    - "effective character augmented word embedding for machine reading comprehension"
    - "subword augmented embedding for cloze reading comprehension"
    - "one shot learning for question answering in gaokao history challenge"
    - "dynamic sentence sampling for efficient training of neural machine translation"
    - "character word lstm language models"

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)