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: "science question answering using instructional materials"
  Abstract: We provide a solution for elementary science test using instructional materials. We posit that there is a hidden structure that explains the correctness of an answer given the question and instructional materials and present a unified max-margin framework that learns to find these hidden structures (given a corpus of question-answer pairs and instructional materials), and uses what it learns to answer novel elementary science questions. Our evaluation shows that our framework outperforms several strong baselines.

Connected through hyperedge HE-1 (induced by source paper "towards understanding and answering multi sentence recommendation questions on tourism"):
  with members:
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "question answering on knowledge bases and text using universal schema and memory networks"
    - "reading wikipedia to answer open domain questions"
    - "answering complex questions using open information extraction"
    - "newsqa a machine comprehension dataset"
    - "squad 100 000 questions for machine comprehension of text"
    - "question answering on freebase via relation extraction and textual evidence"
    - "word embedding based correlation model for question answer matching"

Connected through hyperedge HE-2 (induced by source paper "crowdsourcing multiple choice science questions"):
  with members:
    - "question answering via integer programming over semi structured knowledge"
    - "wikireading a novel large scale language understanding task over wikipedia"
    - "text understanding with the attention sum reader network"
    - "squad 100 000 questions for machine comprehension of text"
    - "the lambada dataset word prediction requiring a broad discourse context"
    - "moving beyond the turing test with the allen ai science challenge"
    - "the goldilocks principle reading children s books with explicit memory representations"
    - "a large annotated corpus for learning natural language inference"

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)