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: "iterative alternating neural attention for machine reading"
  Abstract: Described herein are systems and methods for providing a natural language comprehension system (NLCS) that iteratively performs an alternating search to gather information that may be used to predict the answer to the question. The NLCS first attends to a query glimpse of the question, and then finds one or more corresponding matches by attending to a text glimpse of the text.

Connected through hyperedge HE-1 (induced by source paper "multi step reasoning via recurrent dual attention for visual dialog"):
  with members:
    - "image question answer synergistic network for visual dialog"
    - "reasoning visual dialogs with structural and partial observations"
    - "making history matter history advantage sequence training for visual dialog"
    - "dual attention networks for visual reference resolution in visual dialog"
    - "generative visual dialogue system via adaptive reasoning and weighted likelihood estimation"
    - "factor graph attention"
    - "relation aware graph attention network for visual question answering"
    - "flipdial a generative model for two way visual dialogue"

Connected through hyperedge HE-2 (induced by source paper "contextual recurrent units for cloze style reading comprehension"):
  with members:
    - "dataset and neural recurrent sequence labeling model for open domain factoid question answering"
    - "reasoning with memory augmented neural networks for language comprehension"
    - "text understanding with the attention sum reader network"
    - "dependency sensitive convolutional neural networks for modeling sentences and documents"
    - "consensus attention based neural networks for chinese reading comprehension"
    - "words or characters fine grained gating for reading comprehension"
    - "ac blstm asymmetric convolutional bidirectional lstm networks for text classification"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-3 (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"
    - "lingke a fine grained multi turn chatbot for customer service"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"

Connected through hyperedge HE-4 (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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "iterative recursive attention model for interpretable sequence classification"):
  with members:
    - "on the convergence of adam and beyond"
    - "deep contextualized word representations"
    - "learning to generate reviews and discovering sentiment"
    - "explaining recurrent neural network predictions in sentiment analysis"
    - "attention is all you need"
    - "non autoregressive neural machine translation"
    - "learned in translation contextualized word vectors"
    - "extracting automata from recurrent neural networks using queries and counterexamples"

Connected through hyperedge HE-6 (induced by source paper "direct optimization of f measure for retrieval based personal question answering"):
  with members:
    - "constrained convolutional recurrent networks to improve speech quality with low impact on recognition accuracy"
    - "attention is all you need"
    - "reinforcement learning to adapt speech enhancement to instantaneous input signal quality"
    - "advances in pre training distributed word representations"
    - "bilateral multi perspective matching for natural language sentences"
    - "neural models for information retrieval"
    - "bidirectional attention flow for machine comprehension"
    - "squad 100 000 questions for machine comprehension of text"

Connected through hyperedge HE-7 (induced by source paper "stochastic answer networks for natural language inference"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "multi task learning for machine reading comprehension"
    - "natural language inference over interaction space"
    - "an empirical analysis of multiple turn reasoning strategies in reading comprehension tasks"
    - "attention is all you need"
    - "stochastic answer networks for machine reading comprehension"

Connected through hyperedge HE-8 (induced by source paper "exploiting sentence embedding for medical question answering"):
  with members:
    - "weaver deep co encoding of questions and documents for machine reading"
    - "multi passage machine reading comprehension with cross passage answer verification"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "medical exam question answering with large scale reading comprehension"
    - "a structured self attentive sentence embedding"
    - "reading wikipedia to answer open domain questions"
    - "long short term memory networks for machine reading"
    - "learning distributed representations of sentences from unlabelled data"

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)