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: "coarse grain fine grain coattention network for multi evidence question answering"
  Abstract: End-to-end neural models have made significant progress in question answering, however recent studies show that these models implicitly assume that the answer and evidence appear close together in a single document. In this work, we propose the Coarse-grain Fine-grain Coattention Network (CFC), a new question answering model that combines information from evidence across multiple documents. The CFC consists of a coarse-grain module that interprets documents with respect to the query then finds a relevant answer, and a fine-grain module which scores each candidate answer by comparing its occurrences across all of the documents with the query. We design these modules using hierarchies of coattention and self-attention, which learn to emphasize different parts of the input. On the Qangaroo WikiHop multi-evidence question answering task, the CFC obtains a new state-of-the-art result of 70.6% on the blind test set, outperforming the previous best by 3% accuracy despite not using pretrained contextual encoders.

Connected through hyperedge HE-1 (induced by source paper "answering while summarizing multi task learning for multi hop qa with evidence extraction"):
  with members:
    - "team papelo transformer networks at fever"
    - "cognitive graph for multi hop reading comprehension at scale"
    - "evidence sentence extraction for machine reading comprehension"
    - "dynamically fused graph network for multi hop reasoning"
    - "combining fact extraction and verification with neural semantic matching networks"
    - "the fact extraction and verification fever shared task"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "ukp athene multi sentence textual entailment for claim verification"

Connected through hyperedge HE-2 (induced by source paper "multi hop reading comprehension across multiple documents by reasoning over heterogeneous graphs"):
  with members:
    - "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"
    - "graph convolutional networks for text classification"

Connected through hyperedge HE-3 (induced by source paper "multi hop question answering via reasoning chains"):
  with members:
    - "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"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"

Connected through hyperedge HE-4 (induced by source paper "self assembling modular networks for interpretable multi hop reasoning"):
  with members:
    - "compositional questions do not necessitate multi hop reasoning"
    - "understanding dataset design choices for multi hop reasoning"
    - "multi hop reading comprehension through question decomposition and rescoring"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "dynamic knowledge graph construction for zero shot commonsense question answering"):
  with members:
    - "explore propose and assemble an interpretable model for multi hop reading comprehension"
    - "using local knowledge graph construction to scale seq2seq models to multi document inputs"
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "self assembling modular networks for interpretable multi hop reasoning"
    - "kagnet knowledge aware graph networks for commonsense reasoning"
    - "ranking and selecting multi hop knowledge paths to better predict human needs"
    - "modeling naive psychology of characters in simple commonsense stories"
    - "learning to write with cooperative discriminators"

Connected through hyperedge HE-6 (induced by source paper "nlprolog reasoning with weak unification for question answering in natural language"):
  with members:
    - "weaver deep co encoding of questions and documents for machine reading"
    - "dynamic integration of background knowledge in neural nlu systems"
    - "neural models for reasoning over multiple mentions using coreference"
    - "question answering by reasoning across documents with graph convolutional networks"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "a survey on open information extraction"
    - "learning explanatory rules from noisy data"
    - "neural symbolic learning and reasoning a survey and interpretation"

Connected through hyperedge HE-7 (induced by source paper "select answer and explain interpretable multi hop reading comprehension over multiple documents"):
  with members:
    - "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"
    - "an interpretable reasoning network for multi relation question answering"

Connected through hyperedge HE-8 (induced by source paper "explore propose and assemble an interpretable model for multi hop reading comprehension"):
  with members:
    - "weaver deep co encoding of questions and documents for machine reading"
    - "multi hop knowledge graph reasoning with reward shaping"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "neural models for reasoning over multiple mentions using coreference"
    - "know what you don t know unanswerable questions for squad"
    - "deep contextualized word representations"
    - "question answering by reasoning across documents with graph convolutional networks"
    - "bert pre training of deep bidirectional transformers for language understanding"

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)