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: "multiresolution graph attention networks for relevance matching"
  Abstract: A large number of deep learning models have been proposed for the text matching problem, which is at the core of various typical natural language processing (NLP) tasks. However, existing deep models are mainly designed for the semantic matching between a pair of short texts, such as paraphrase identification and question answering, and do not perform well on the task of relevance matching between short-long text pairs. This is partially due to the fact that the essential characteristics of short-long text matching have not been well considered in these deep models. More specifically, these methods fail to handle extreme length discrepancy between text pieces and neither can they fully characterize the underlying structural information in long text documents. In this paper, we are especially interested in relevance matching between a piece of short text and a long document, which is critical to problems like query-document matching in information retrieval and web searching. To extract the structural information of documents, an undirected graph is constructed, with each vertex representing a keyword and the weight of an edge indicating the degree of interaction between keywords. Based on the keyword graph, we further propose a Multiresolution Graph Attention Network to learn multi-layered representations of vertices through a Graph Convolutional Network (GCN), and then match the short text snippet with the graphical representation of the document with an attention mechanism applied over each layer of the GCN. Experimental results on two datasets demonstrate that our graph approach outperforms other state-of-the-art deep matching models.

Connected through hyperedge HE-1 (induced by source paper "learning to generate questions by learningwhat not to generate"):
  with members:
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "difficulty controllable question generation for reading comprehension"
    - "improving neural question generation using answer separation"
    - "relational inductive biases deep learning and graph networks"
    - "difficulty controllable generation of reading comprehension questions"
    - "harvesting paragraph level question answer pairs from wikipedia"
    - "sequential copying networks"
    - "neural models for key phrase detection and question generation"

Connected through hyperedge HE-2 (induced by source paper "coherent comment generation for chinese articles with a graph to sequence model"):
  with members:
    - "graph neural networks a review of methods and applications"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "sql to text generation with graph to sequence model"
    - "graph convolutional neural networks for web scale recommender systems"
    - "from eliza to xiaoice challenges and opportunities with social chatbots"
    - "graph to sequence learning using gated graph neural networks"
    - "graph convolutional networks for text classification"
    - "rethinking knowledge graph propagation for zero shot learning"

Connected through hyperedge HE-3 (induced by source paper "dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "socialgcn an efficient graph convolutional network based model for social recommendation"
    - "deepinf social influence prediction with deep learning"
    - "collaborative filtering with social exposure a modular approach to social recommendation"
    - "attention is all you need"
    - "neural collaborative filtering"
    - "item silk road recommending items from information domains to social users"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "matching article pairs with graphical decomposition and convolutions"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "matching natural language sentences with hierarchical sentence factorization"
    - "growing story forest online from massive breaking news"
    - "representation learning on graphs methods and applications"
    - "matchzoo a toolkit for deep text matching"
    - "learning to match using local and distributed representations of text for web search"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "text matching as image recognition"

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)