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: "crowdsourcing multiple choice science questions"
  Abstract: We present a novel method for obtaining high-quality, domain-targeted multiple choice questions from crowd workers. Generating these questions can be difficult without trading away originality, relevance or diversity in the answer options. Our method addresses these problems by leveraging a large corpus of domain-specific text and a small set of existing questions. It produces model suggestions for document selection and answer distractor choice which aid the human question generation process. With this method we have assembled SciQ, a dataset of 13.7K multiple choice science exam questions (Dataset available at this http URL). We demonstrate that the method produces in-domain questions by providing an analysis of this new dataset and by showing that humans cannot distinguish the crowdsourced questions from original questions. When using SciQ as additional training data to existing questions, we observe accuracy improvements on real science exams.

Connected through hyperedge HE-1 (induced by source paper "improving question answering with external knowledge"):
  with members:
    - "end to end open domain question answering with"
    - "yuanfudao at semeval 2018 task 11 three way attention and relational knowledge for commonsense machine comprehension"
    - "improving retrieval based question answering with deep inference models"
    - "improving question answering by commonsense based pre training"
    - "ranking paragraphs for improving answer recall in open domain question answering"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "generating wikipedia by summarizing long sequences"
    - "what makes reading comprehension questions easier"

Connected through hyperedge HE-2 (induced by source paper "what does my qa model know devising controlled probes using expert knowledge"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "overcoming catastrophic forgetting by neuron level plasticity control"
    - "abductive commonsense reasoning"
    - "inoculation by fine tuning a method for analyzing challenge datasets"
    - "from f to a on the n y regents science exams an overview of the aristo project"
    - "diversify your datasets analyzing generalization via controlled variance in adversarial datasets"
    - "qasc a dataset for question answering via sentence composition"
    - "wic the word in context dataset for evaluating context sensitive meaning representations"

Connected through hyperedge HE-3 (induced by source paper "unsupervised domain adaptation of language models for reading comprehension"):
  with members:
    - "bert rediscovers the classical nlp pipeline"
    - "learning and evaluating general linguistic intelligence"
    - "simplified neural unsupervised domain adaptation"
    - "adagraph unifying predictive and continuous domain adaptation through graphs"
    - "multi task deep neural networks for natural language understanding"
    - "mrqa 2019 shared task evaluating generalization in reading comprehension"
    - "zero shot domain adaptation based on attribute information"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-4 (induced by source paper "investigating crowdsourcing to generate distractors for multiple choice assessments"):
  with members:
    - "creating a cybersecurity concept inventory a status report on the cats project"

Connected through hyperedge HE-5 (induced by source paper "coqa a conversational question answering challenge"):
  with members:
    - "multi relational question answering from narratives machine reading and reasoning in simulated worlds"
    - "interpretation of natural language rules in conversational machine reading"
    - "end to end audio visual scene aware dialog using multimodal attention based video features"
    - "the web as a knowledge base for answering complex questions"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "hierarchical neural story generation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "complex sequential question answering towards learning to converse over linked question answer pairs with a knowledge graph"

Connected through hyperedge HE-6 (induced by source paper "answering science exam questions using query rewriting with background knowledge"):
  with members:
    - "query expansion techniques for information retrieval a survey"
    - "improving question answering by commonsense based pre training"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "kg 2 learning to reason science exam questions with contextual knowledge graph embeddings"
    - "know what you don t know unanswerable questions for squad"
    - "end task oriented textual entailment via deep explorations of inter sentence interactions"
    - "improving natural language inference using external knowledge in the science questions domain"
    - "improving machine reading comprehension with general reading strategies"

Connected through hyperedge HE-7 (induced by source paper "improving natural language inference using external knowledge in the science questions domain"):
  with members:
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "open domain question answering using early fusion of knowledge bases and text"
    - "adventure adversarial training for textual entailment with knowledge guided examples"
    - "evidence aggregation for answer re ranking in open domain question answering"
    - "neural natural language inference models enhanced with external knowledge"
    - "textual entailment with structured attentions and composition"
    - "comparative study of cnn and rnn for natural language processing"

Connected through hyperedge HE-8 (induced by source paper "a systematic classification of knowledge reasoning and context within the arc dataset"):
  with members:
    - "question answering as global reasoning over semantic abstractions"
    - "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"
    - "reading wikipedia to answer open domain questions"
    - "simple and effective multi paragraph reading comprehension"
    - "newsqa a machine comprehension dataset"

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.HC(Human-Computer Interaction)