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: "reasoning in vector space an exploratory study of question answering"
  Abstract: Question answering tasks have shown remarkable progress with distributed vector representation. In this paper, we investigate the recently proposed Facebook bAbI tasks which consist of twenty different categories of questions that require complex reasoning. Because the previous work on bAbI are all end-to-end models, errors could come from either an imperfect understanding of semantics or in certain steps of the reasoning. For clearer analysis, we propose two vector space models inspired by Tensor Product Representation (TPR) to perform knowledge encoding and logical reasoning based on common-sense inference. They together achieve near-perfect accuracy on all categories including positional reasoning and path finding that have proved difficult for most of the previous approaches. We hypothesize that the difficulties in these categories are due to the multi-relations in contrast to uni-relational characteristic of other categories. Our exploration sheds light on designing more sophisticated dataset and moving one step toward integrating transparent and interpretable formalism of TPR into existing learning paradigms.

Connected through hyperedge HE-1 (induced by source paper "neural text summarization a critical evaluation"):
  with members:
    - "what comes next extractive summarization by next sentence prediction"
    - "neural extractive text summarization with syntactic compression"
    - "improving abstraction in text summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "content selection in deep learning models of summarization"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "annotation artifacts in natural language inference data"
    - "generating wikipedia by summarizing long sequences"

Connected through hyperedge HE-2 (induced by source paper "multi relational question answering from narratives machine reading and reasoning in simulated worlds"):
  with members:
    - "a knowledge grounded neural conversation model"
    - "race large scale reading comprehension dataset from examinations"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "searchqa a new q a dataset augmented with context from a search engine"
    - "the narrativeqa reading comprehension challenge"
    - "answering complex questions using open information extraction"
    - "newsqa a machine comprehension dataset"
    - "a thorough examination of the cnn daily mail reading comprehension task"

Connected through hyperedge HE-3 (induced by source paper "hubert untangles bert to improve transfer across nlp tasks"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "semantics aware bert for language understanding"
    - "are sixteen heads really better than one"
    - "visualizing and measuring the geometry of bert"
    - "bert rediscovers the classical nlp pipeline"
    - "multi task deep neural networks for natural language understanding"
    - "open sesame getting inside bert s linguistic knowledge"
    - "unifying question answering text classification and regression via span extraction"

Connected through hyperedge HE-4 (induced by source paper "natural to formal language generation using tensor product representations"):
  with members:
    - "core semantic first a top down approach for amr parsing"
    - "ain t nobody got time for coding structure aware program synthesis from natural language"
    - "attentive tensor product learning"
    - "neural approaches to conversational ai"
    - "neural program search solving programming tasks from description and examples"
    - "learning to reason with third order tensor products"
    - "a survey on semantic parsing"
    - "question answering with grammatically interpretable representations"

Connected through hyperedge HE-5 (induced by source paper "how much reading does reading comprehension require a critical investigation of popular benchmarks"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "hypothesis only baselines in natural language inference"
    - "race large scale reading comprehension dataset from examinations"
    - "newsqa a machine comprehension dataset"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "text understanding with the attention sum reader network"
    - "key value memory networks for directly reading documents"

Connected through hyperedge HE-6 (induced by source paper "working memory networks augmenting memory networks with a relational reasoning module"):
  with members:
    - "recurrent relational networks for complex relational reasoning"
    - "attention is all you need"
    - "a simple neural network module for relational reasoning"
    - "tracking the world state with recurrent entity networks"
    - "dynamic memory networks for visual and textual question answering"
    - "interaction networks for learning about objects relations and physics"
    - "end to end memory networks"
    - "towards ai complete question answering a set of prerequisite toy tasks"

Connected through hyperedge HE-7 (induced by source paper "evaluating theory of mind in question answering"):
  with members:
    - "machine theory of mind"
    - "a simple neural network module for relational reasoning"
    - "it takes two to tango towards theory of ai s mind"
    - "who is mistaken"
    - "scaling memory augmented neural networks with sparse reads and writes"
    - "tracking the world state with recurrent entity networks"
    - "end to end memory networks"
    - "towards ai complete question answering a set of prerequisite toy tasks"

Connected through hyperedge HE-8 (induced by source paper "learning and analyzing vector encoding of symbolic representations"):
  with members:
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "learning to execute"

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)