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: "one shot learning for question answering in gaokao history challenge"
  Abstract: Answering questions from university admission exams (Gaokao in Chinese) is a challenging AI task since it requires effective representation to capture complicated semantic relations between questions and answers. In this work, we propose a hybrid neural model for deep question-answering task from history examinations. Our model employs a cooperative gated neural network to retrieve answers with the assistance of extra labels given by a neural turing machine labeler. Empirical study shows that the labeler works well with only a small training dataset and the gated mechanism is good at fetching the semantic representation of lengthy answers. Experiments on question answering demonstrate the proposed model obtains substantial performance gains over various neural model baselines in terms of multiple evaluation metrics.

Connected through hyperedge HE-1 (induced by source paper "sg net syntax guided machine reading comprehension"):
  with members:
    - "semantics aware bert for language understanding"
    - "limit bert linguistic informed multi task bert"
    - "syntax aware neural semantic role labeling with supertags"
    - "global greedy dependency parsing"
    - "option comparison network for multiple choice reading comprehension"
    - "probing contextualized sentence representations with visual awareness"
    - "what does bert look at an analysis of bert s attention"
    - "dual co matching network for multi choice reading comprehension"

Connected through hyperedge HE-2 (induced by source paper "head driven phrase structure grammar parsing on penn treebank"):
  with members:
    - "lattice based transformer encoder for neural machine translation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "what s going on in neural constituency parsers an analysis"
    - "two local models for neural constituent parsing"
    - "exploring recombination for efficient decoding of neural machine translation"
    - "multilingual constituency parsing with self attention and pre training"
    - "deep contextualized word representations"
    - "subword augmented embedding for cloze reading comprehension"

Connected through hyperedge HE-3 (induced by source paper "investigating prior knowledge for challenging chinese machine reading comprehension"):
  with members:
    - "boolq exploring the surprising difficulty of natural yes no questions"
    - "ernie enhanced representation through knowledge integration"
    - "geosqa a benchmark for scenario based question answering in the geography domain at high school level"
    - "pre training with whole word masking for chinese bert"
    - "dream a challenge dataset and models for dialogue based reading comprehension"
    - "chid a large scale chinese idiom dataset for cloze test"
    - "analogical reasoning on chinese morphological and semantic relations"
    - "exploiting sentence embedding for medical question answering"

Connected through hyperedge HE-4 (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-5 (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"
    - "multi passage machine reading comprehension with cross passage answer verification"
    - "modeling multi turn conversation with deep utterance aggregation"
    - "lingke a fine grained multi turn chatbot for customer service"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "attention over attention neural networks for reading comprehension"

Connected through hyperedge HE-6 (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"
    - "sjtu nlp at semeval 2018 task 9 neural hypernym discovery with term embeddings"
    - "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-7 (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"
    - "large scale qa srl parsing"
    - "multi passage machine reading comprehension with cross passage answer verification"

Connected through hyperedge HE-8 (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"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "sjtu nlp at semeval 2018 task 9 neural hypernym discovery with term embeddings"
    - "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)