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: "question answering as global reasoning over semantic abstractions"
  Abstract: We propose a novel method for exploiting the semantic structure of text to answer multiple-choice questions. The approach is especially suitable for domains that require reasoning over a diverse set of linguistic constructs but have limited training data. To address these challenges, we present the first system, to the best of our knowledge, that reasons over a wide range of semantic abstractions of the text, which are derived using off-the-shelf, general-purpose, pre-trained natural language modules such as semantic role labelers, coreference resolvers, and dependency parsers. Representing multiple abstractions as a family of graphs, we translate question answering (QA) into a search for an optimal subgraph that satisfies certain global and local properties. This formulation generalizes several prior structured QA systems. Our system, SEMANTICILP, demonstrates strong performance on two domains simultaneously. In particular, on a collection of challenging science QA datasets, it outperforms various state-of-the-art approaches, including neural models, broad coverage information retrieval, and specialized techniques using structured knowledge bases, by 2%-6%.

Connected through hyperedge HE-1 (induced by source paper "bridging knowledge graphs to generate scene graphs"):
  with members:
    - "knowledge embedded routing network for scene graph generation"
    - "entity relation and event extraction with contextualized span representations"
    - "scene graph generation with external knowledge and image reconstruction"
    - "learning by abstraction the neural state machine"
    - "an empirical study on leveraging scene graphs for visual question answering"
    - "class balanced loss based on effective number of samples"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "linknet relational embedding for scene graph"

Connected through hyperedge HE-2 (induced by source paper "reasoning driven question answering for natural language understanding"):
  with members:
    - "on the capabilities and limitations of reasoning for natural language understanding"
    - "evidence sentence extraction for machine reading comprehension"
    - "going on a vacation takes longer than going for a walk a study of temporal commonsense understanding"
    - "cogcomptime a tool for understanding time in natural language text"
    - "annotation artifacts in natural language inference data"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "multi hop knowledge graph reasoning with reward shaping"
    - "hypothesis only baselines in natural language inference"

Connected through hyperedge HE-3 (induced by source paper "quick and not so dirty unsupervised selection of justification sentences for multi hop question answering"):
  with members:
    - "improving question answering with external knowledge"
    - "understanding dataset design choices for multi hop reasoning"
    - "evidence sentence extraction for machine reading comprehension"
    - "dream a challenge dataset and models for dialogue based reading comprehension"
    - "repurposing entailment for multi hop question answering tasks"
    - "an interpretable reasoning network for multi relation question answering"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "transfer learning for context aware question matching in information seeking conversations in e commerce"

Connected through hyperedge HE-4 (induced by source paper "qasc a dataset for question answering via sentence composition"):
  with members:
    - "on the capabilities and limitations of reasoning for natural language understanding"
    - "improving question answering with external knowledge"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "what s missing a knowledge gap guided approach for multi hop question answering"
    - "a systematic classification of knowledge reasoning and context within the arc dataset"
    - "from recognition to cognition visual commonsense reasoning"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"

Connected through hyperedge HE-5 (induced by source paper "on the capabilities and limitations of reasoning for natural language understanding"):
  with members:
    - "multi hop knowledge graph reasoning with reward shaping"
    - "worldtree a corpus of explanation graphs for elementary science questions supporting multi hop inference"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal semantic parsing"
    - "answering complex questions using open information extraction"
    - "question answering via integer programming over semi structured knowledge"
    - "chains of reasoning over entities relations and text using recurrent neural networks"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-6 (induced by source paper "playing by the book an interactive game approach for action graph extraction from text"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "a survey of domain adaptation for neural machine translation"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "textworld a learning environment for text based games"
    - "building dynamic knowledge graphs from text using machine reading comprehension"
    - "deep dyna q integrating planning for task completion dialogue policy learning"
    - "learn what not to learn action elimination with deep reinforcement learning"
    - "open domain question answering using early fusion of knowledge bases and text"

Connected through hyperedge HE-7 (induced by source paper "a systematic classification of knowledge reasoning and context within the arc dataset"):
  with members:
    - "worldtree a corpus of explanation graphs for elementary science questions supporting multi hop inference"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "searchqa a new q a dataset augmented with context from a search engine"
    - "learning recurrent span representations for extractive question answering"
    - "crowdsourcing multiple choice science questions"
    - "reading wikipedia to answer open domain questions"
    - "simple and effective multi paragraph reading comprehension"
    - "newsqa a machine comprehension dataset"

Connected through hyperedge HE-8 (induced by source paper "attentive semantic role labeling with boundary indicator"):
  with members:
    - "effective character augmented word embedding for machine reading comprehension"
    - "deep contextualized word representations"
    - "subword augmented embedding for cloze reading comprehension"
    - "one shot learning for question answering in gaokao history challenge"
    - "modeling multi turn conversation with deep utterance aggregation"
    - "a simple and accurate syntax agnostic neural model for dependency based semantic role labeling"
    - "deep semantic role labeling with self attention"
    - "encoding sentences with graph convolutional networks for semantic role labeling"

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)