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: "repartitioning of the complexwebquestions dataset"
  Abstract: Recently, Talmor and Berant (2018) introduced ComplexWebQuestions - a dataset focused on answering complex questions by decomposing them into a sequence of simpler questions and extracting the answer from retrieved web snippets. In their work the authors used a pre-trained reading comprehension (RC) model (Salant and Berant, 2018) to extract the answer from the web snippets. In this short note we show that training a RC model directly on the training data of ComplexWebQuestions reveals a leakage from the training set to the test set that allows to obtain unreasonably high performance. As a solution, we construct a new partitioning of ComplexWebQuestions that does not suffer from this leakage and publicly release it. We also perform an empirical evaluation on these two datasets and show that training a RC model on the training data substantially improves state-of-the-art performance.

Connected through hyperedge HE-1 (induced by source paper "multiqa an empirical investigation of generalization and transfer in reading comprehension"):
  with members:
    - "multi step retriever reader interaction for scalable open domain question answering"
    - "learning and evaluating general linguistic intelligence"
    - "cognitive graph for multi hop reading comprehension at scale"
    - "multi task deep neural networks for natural language understanding"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "an embarrassingly simple approach for transfer learning from pretrained language models"
    - "densely connected attention propagation for reading comprehension"
    - "sentence encoders on stilts supplementary training on intermediate labeled data tasks"

Connected through hyperedge HE-2 (induced by source paper "do multi hop readers dream of reasoning chains"):
  with members:
    - "coarse grain fine grain coattention network for multi evidence question answering"
    - "compositional questions do not necessitate multi hop reasoning"
    - "dual co matching network for multi choice reading comprehension"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "question answering by reasoning across documents with graph convolutional networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "exploring graph structured passage representation for multi hop reading comprehension with graph neural networks"
    - "a co matching model for multi choice reading comprehension"

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)