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 across multiple documents by reasoning over heterogeneous graphs"
  Abstract: Multi-hop reading comprehension (RC) across documents poses new challenge over single-document RC because it requires reasoning over multiple documents to reach the final answer. In this paper, we propose a new model to tackle the multi-hop RC problem. We introduce a heterogeneous graph with different types of nodes and edges, which is named as Heterogeneous Document-Entity (HDE) graph. The advantage of HDE graph is that it contains different granularity levels of information including candidates, documents and entities in specific document contexts. Our proposed model can do reasoning over the HDE graph with nodes representation initialized with co-attention and self-attention based context encoders. We employ Graph Neural Networks (GNN) based message passing algorithms to accumulate evidences on the proposed HDE graph. Evaluated on the blind test set of the Qangaroo WikiHop data set, our HDE graph based single model delivers competitive result, and the ensemble model achieves the state-of-the-art performance.

Connected through hyperedge HE-1 (induced by source paper "tensor graph convolutional networks for text classification"):
  with members:
    - "mr gnn multi resolution and dual graph neural network for predicting structured entity interactions"
    - "multi channel graph neural network for entity alignment"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "relational inductive biases deep learning and graph networks"
    - "joint embedding of words and labels for text classification"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "incorporating syntactic and semantic information in word embeddings using graph convolutional networks"
    - "graph convolutional encoders for syntax aware neural machine translation"

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 "improving graph attention networks with large margin based constraints"):
  with members:
    - "simplifying graph convolutional networks"
    - "a comprehensive survey on graph neural networks"
    - "link prediction based on graph neural networks"
    - "graph convolutional neural networks for web scale recommender systems"
    - "deeply learning molecular structure property relationships using attention and gate augmented graph convolutional network"
    - "geniepath graph neural networks with adaptive receptive paths"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "gaan gated attention networks for learning on large and spatiotemporal graphs"

Connected through hyperedge HE-4 (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-5 (induced by source paper "graph based reasoning over heterogeneous external knowledge for commonsense question answering"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "explore propose and assemble an interpretable model for multi hop reading comprehension"
    - "kagnet knowledge aware graph networks for commonsense reasoning"
    - "explain yourself leveraging language models for commonsense reasoning"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "align mask and select a simple method for incorporating commonsense knowledge into language representation models"
    - "roberta a robustly optimized bert pretraining approach"
    - "mcscript2 0 a machine comprehension corpus focused on script events and participants"

Connected through hyperedge HE-6 (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"
    - "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-7 (induced by source paper "selective attention based graph convolutional networks for aspect level sentiment classification"):
  with members:
    - "coarse grain fine grain coattention network for multi evidence question answering"
    - "targeted sentiment classification with attentional encoder network"
    - "densely connected graph convolutional networks for graph to sequence learning"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"
    - "attention guided graph convolutional networks for relation extraction"
    - "modeling sentiment dependencies with graph convolutional networks for aspect level sentiment classification"
    - "exploiting coarse to fine task transfer for aspect level sentiment classification"
    - "graph convolution over pruned dependency trees improves relation extraction"

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)