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: "exploring graph structured passage representation for multi hop reading comprehension with graph neural networks"
  Abstract: Multi-hop reading comprehension focuses on one type of factoid question, where a system needs to properly integrate multiple pieces of evidence to correctly answer a question. Previous work approximates global evidence with local coreference information, encoding coreference chains with DAG-styled GRU layers within a gated-attention reader. However, coreference is limited in providing information for rich inference. We introduce a new method for better connecting global evidence, which forms more complex graphs compared to DAGs. To perform evidence integration on our graphs, we investigate two recent graph neural networks, namely graph convolutional network (GCN) and graph recurrent network (GRN). Experiments on two standard datasets show that richer global information leads to better answers. Our method performs better than all published results on these datasets.

Connected through hyperedge HE-1 (induced by source paper "coarse grain fine grain coattention network for multi evidence question answering"):
  with members:
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "efficient and robust question answering from minimal context over documents"
    - "neural models for reasoning over multiple mentions using coreference"
    - "global locally self attentive dialogue state tracker"
    - "deep contextualized word representations"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "question answering by reasoning across documents with graph convolutional networks"
    - "coarse to fine decoding for neural semantic parsing"

Connected through hyperedge HE-2 (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-3 (induced by source paper "stability and generalization of graph convolutional neural networks"):
  with members:
    - "adaptive graph convolutional neural networks"
    - "hierarchical graph representation learning with differentiable pooling"
    - "how powerful are graph neural networks"
    - "covariant compositional networks for learning graphs"
    - "unifying local and non local signal processing with graph cnns"
    - "deriving neural architectures from sequence and graph kernels"
    - "exploring generalization in deep learning"
    - "splinecnn fast geometric deep learning with continuous b spline kernels"

Connected through hyperedge HE-4 (induced by source paper "multi hop reading comprehension across multiple documents by reasoning over heterogeneous graphs"):
  with members:
    - "coarse grain fine grain coattention network for multi evidence question answering"
    - "global to local memory pointer networks for task oriented dialogue"
    - "densely connected attention propagation for reading comprehension"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "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"
    - "exploiting semantics in neural machine translation with graph convolutional networks"
    - "neural models for reasoning over multiple mentions using coreference"

Connected through hyperedge HE-5 (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"
    - "multi hop reading comprehension through question decomposition and rescoring"

Connected through hyperedge HE-6 (induced by source paper "identifying supporting facts for multi hop question answering with document graph networks"):
  with members:
    - "transforming complex sentences into a semantic hierarchy"
    - "graph neural networks a review of methods and applications"
    - "bilinear attention networks"
    - "graphene semantically linked propositions in open information extraction"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "learning to search in long documents using document structure"
    - "know what you don t know unanswerable questions for squad"
    - "relational inductive biases deep learning and graph networks"

Connected through hyperedge HE-7 (induced by source paper "graphflow exploiting conversation flow with graph neural networks for conversational machine comprehension"):
  with members:
    - "sdnet contextualized attention based deep network for conversational question answering"
    - "sql to text generation with graph to sequence model"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "a qualitative comparison of coqa squad 2 0 and quac"
    - "exploiting rich syntactic information for semantic parsing with graph to sequence model"
    - "contextualized non local neural networks for sequence learning"
    - "coqa a conversational question answering challenge"
    - "graph2seq graph to sequence learning with attention based neural networks"

Connected through hyperedge HE-8 (induced by source paper "select answer and explain interpretable multi hop reading comprehension over multiple documents"):
  with members:
    - "coarse grain fine grain coattention network for multi evidence question answering"
    - "bag bi directional attention entity graph convolutional network for multi hop reasoning question answering"
    - "answering while summarizing multi task learning for multi hop qa with evidence extraction"
    - "understanding dataset design choices for multi hop reasoning"
    - "evidence sentence extraction for machine reading comprehension"
    - "multi hop reading comprehension across multiple documents by reasoning over heterogeneous graphs"
    - "roberta a robustly optimized bert pretraining approach"
    - "densely connected attention propagation for 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)