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: "weaver deep co encoding of questions and documents for machine reading"
  Abstract: This paper aims at improving how machines can answer questions directly from text, with the focus of having models that can answer correctly multiple types of questions and from various types of texts, documents or even from large collections of them. To that end, we introduce the Weaver model that uses a new way to relate a question to a textual context by weaving layers of recurrent networks, with the goal of making as few assumptions as possible as to how the information from both question and context should be combined to form the answer. We show empirically on six datasets that Weaver performs well in multiple conditions. For instance, it produces solid results on the very popular SQuAD dataset (Rajpurkar et al., 2016), solves almost all bAbI tasks (Weston et al., 2015) and greatly outperforms state-of-the-art methods for open domain question answering from text (Chen et al., 2017).

Connected through hyperedge HE-1 (induced by source paper "learning to retrieve reasoning paths over wikipedia graph for question answering"):
  with members:
    - "latent retrieval for weakly supervised open domain question answering"
    - "passage re ranking with bert"
    - "do multi hop readers dream of reasoning chains"
    - "pullnet open domain question answering with iterative retrieval on knowledge bases and text"
    - "compositional questions do not necessitate multi hop reasoning"
    - "cognitive graph for multi hop reading comprehension at scale"
    - "multi hop paragraph retrieval for open domain question answering"
    - "retrieve read rerank towards end to end multi document reading comprehension"

Connected through hyperedge HE-2 (induced by source paper "nlprolog reasoning with weak unification for question answering in natural language"):
  with members:
    - "coarse grain fine grain coattention network for multi evidence question answering"
    - "dynamic integration of background knowledge in neural nlu systems"
    - "neural models for reasoning over multiple mentions using coreference"
    - "question answering by reasoning across documents with graph convolutional networks"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "a survey on open information extraction"
    - "learning explanatory rules from noisy data"
    - "neural symbolic learning and reasoning a survey and interpretation"

Connected through hyperedge HE-3 (induced by source paper "explore propose and assemble an interpretable model for multi hop reading comprehension"):
  with members:
    - "coarse grain fine grain coattention network for multi evidence question answering"
    - "multi hop knowledge graph reasoning with reward shaping"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "neural models for reasoning over multiple mentions using coreference"
    - "know what you don t know unanswerable questions for squad"
    - "deep contextualized word representations"
    - "question answering by reasoning across documents with graph convolutional networks"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "real time open domain question answering with dense sparse phrase index"):
  with members:
    - "end to end open domain question answering with"
    - "ranking paragraphs for improving answer recall in open domain question answering"
    - "deep contextualized word representations"
    - "retrieve and read multi task learning of information retrieval and reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "evidence aggregation for answer re ranking in open domain question answering"
    - "billion scale similarity search with gpus"
    - "zero shot relation extraction via reading comprehension"

Connected through hyperedge HE-5 (induced by source paper "pervasive attention 2d convolutional neural networks for sequence to sequence prediction"):
  with members:
    - "towards two dimensional sequence to sequence model in neural machine translation"
    - "adversarial neural machine translation"
    - "towards neural phrase based machine translation"
    - "attention is all you need"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "classical structured prediction losses for sequence to sequence learning"
    - "convolutional sequence to sequence learning"
    - "a decomposable attention model for natural language inference"

Connected through hyperedge HE-6 (induced by source paper "open domain question answering using early fusion of knowledge bases and text"):
  with members:
    - "the web as a knowledge base for answering complex questions"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "walk steered convolution for graph classification"
    - "evidence aggregation for answer re ranking in open domain question answering"
    - "reinforced mnemonic reader for machine comprehension"
    - "object oriented neural programming oonp for document understanding"
    - "improved neural relation detection for knowledge base question answering"
    - "question answering on knowledge bases and text using universal schema and memory networks"

Connected through hyperedge HE-7 (induced by source paper "exploiting sentence embedding for medical question answering"):
  with members:
    - "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"
    - "iterative alternating neural attention 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)