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: "multi hop reading comprehension through question decomposition and rescoring"
  Abstract: Multi-hop Reading Comprehension (RC) requires reasoning and aggregation across several paragraphs. We propose a system for multi-hop RC that decomposes a compositional question into simpler sub-questions that can be answered by off-the-shelf single-hop RC models. Since annotations for such decomposition are expensive, we recast sub-question generation as a span prediction problem and show that our method, trained using only 400 labeled examples, generates sub-questions that are as effective as human-authored sub-questions. We also introduce a new global rescoring approach that considers each decomposition (i.e. the sub-questions and their answers) to select the best final answer, greatly improving overall performance. Our experiments on HotpotQA show that this approach achieves the state-of-the-art results, while providing explainable evidence for its decision making in the form of sub-questions.

Connected through hyperedge HE-1 (induced by source paper "unsupervised question decomposition for question answering"):
  with members:
    - "break it down a question understanding benchmark"
    - "unsupervised question answering by cloze translation"
    - "compositional questions do not necessitate multi hop reasoning"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "self assembling modular networks for interpretable multi hop reasoning"
    - "revealing the importance of semantic retrieval for machine reading at scale"
    - "huggingface s transformers state of the art natural language processing"
    - "cross lingual language model pretraining"

Connected through hyperedge HE-2 (induced by source paper "break it down a question understanding benchmark"):
  with members:
    - "bridging the semantic gap with sql query logs in natural language interfaces to databases"
    - "compositional questions do not necessitate multi hop reasoning"
    - "understanding dataset design choices for multi hop reasoning"
    - "towards complex text to sql in cross domain database with intermediate representation"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "answering complex open domain questions through iterative query generation"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "avoiding reasoning shortcuts adversarial evaluation training and model development for multi hop qa"

Connected through hyperedge HE-3 (induced by source paper "do multi hop question answering systems know how to answer the single hop sub questions"):
  with members:
    - "multi paragraph reasoning with knowledge enhanced graph neural network"
    - "select answer and explain interpretable multi hop reading comprehension over multiple documents"
    - "multi hop paragraph retrieval for open domain question answering"
    - "sg net syntax guided machine reading comprehension"
    - "albert a lite bert for self supervised learning of language representations"
    - "dynamically fused graph network for multi hop reasoning"
    - "hierarchical graph network for multi hop question answering"
    - "multi hop question answering via reasoning chains"

Connected through hyperedge HE-4 (induced by source paper "generating followup questions for interpretable multi hop question answering"):
  with members:
    - "multi paragraph reasoning with knowledge enhanced graph neural network"
    - "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"
    - "qasc a dataset for question answering via sentence composition"
    - "capturing greater context for question generation"
    - "revealing the importance of semantic retrieval for machine reading at scale"
    - "hierarchical graph network for multi hop question answering"

Connected through hyperedge HE-5 (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-6 (induced by source paper "hierarchical graph network for multi hop question answering"):
  with members:
    - "answering while summarizing multi task learning for multi hop qa with evidence extraction"
    - "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"
    - "learning to retrieve reasoning paths over wikipedia graph for question answering"
    - "understanding dataset design choices for multi hop reasoning"
    - "revealing the importance of semantic retrieval for machine reading at scale"
    - "huggingface s transformers state of the art natural language processing"

Connected through hyperedge HE-7 (induced by source paper "multi hop question answering via reasoning chains"):
  with members:
    - "coarse grain fine grain coattention network for multi evidence question answering"
    - "explore propose and assemble an interpretable model for multi hop reading comprehension"
    - "compositional questions do not necessitate multi hop reasoning"
    - "understanding dataset design choices for multi hop reasoning"
    - "evidence sentence extraction for machine reading comprehension"
    - "attention is not explanation"
    - "dynamically fused graph network for multi hop reasoning"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"

Connected through hyperedge HE-8 (induced by source paper "self assembling modular networks for interpretable multi hop reasoning"):
  with members:
    - "coarse grain fine grain coattention network for multi evidence question answering"
    - "compositional questions do not necessitate multi hop reasoning"
    - "understanding dataset design choices for multi hop reasoning"
    - "avoiding reasoning shortcuts adversarial evaluation training and model development for multi hop qa"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "using syntax to ground referring expressions in natural images"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "know what you don t know unanswerable questions for squad"

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)