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: "deepgraph graph structure predicts network growth"
  Abstract: The topological (or graph) structures of real-world networks are known to be predictive of multiple dynamic properties of the networks. Conventionally, a graph structure is represented using an adjacency matrix or a set of hand-crafted structural features. These representations either fail to highlight local and global properties of the graph or suffer from a severe loss of structural information. There lacks an effective graph representation, which hinges the realization of the predictive power of network structures. #R##N#In this study, we propose to learn the represention of a graph, or the topological structure of a network, through a deep learning model. This end-to-end prediction model, named DeepGraph, takes the input of the raw adjacency matrix of a real-world network and outputs a prediction of the growth of the network. The adjacency matrix is first represented using a graph descriptor based on the heat kernel signature, which is then passed through a multi-column, multi-resolution convolutional neural network. Extensive experiments on five large collections of real-world networks demonstrate that the proposed prediction model significantly improves the effectiveness of existing methods, including linear or nonlinear regressors that use hand-crafted features, graph kernels, and competing deep learning methods.

Connected through hyperedge HE-1 (induced by source paper "bayesian graph convolutional neural networks using node copying"):
  with members:
    - "fisher bures adversary graph convolutional networks"
    - "residual gated graph convnets"
    - "dual primal graph convolutional networks"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "random graph models for dynamic networks"
    - "robust spatial filtering with graph convolutional neural networks"
    - "cayleynets graph convolutional neural networks with complex rational spectral filters"

Connected through hyperedge HE-2 (induced by source paper "evonet a neural network for predicting the evolution of dynamic graphs"):
  with members:
    - "dynamic graph convolutional networks"
    - "evolvegcn evolving graph convolutional networks for dynamic graphs"
    - "a comprehensive survey on graph neural networks"
    - "link prediction based on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "dyngem deep embedding method for dynamic graphs"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "graphrnn generating realistic graphs with deep auto regressive models"

Connected through hyperedge HE-3 (induced by source paper "graph representation learning via multi task knowledge distillation"):
  with members:
    - "cross stitch networks for multi task learning"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"
    - "line large scale information network embedding"
    - "adam a method for stochastic optimization"
    - "deepwalk online learning of social representations"
    - "predicting the popularity of online content"

Connected through hyperedge HE-4 (induced by source paper "exploring the semantic content of unsupervised graph embeddings an empirical study"):
  with members:
    - "graph embedding techniques applications and performance a survey"
    - "network representation learning a survey"
    - "inductive representation learning on large graphs"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "representation learning on graphs methods and applications"
    - "a survey on network embedding"
    - "learning graph topological features via gan"
    - "a comprehensive survey of graph embedding problems techniques and applications"

Connected through hyperedge HE-5 (induced by source paper "deepcas an end to end predictor of information cascades"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "learning convolutional neural networks for graphs"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "subgraph2vec learning distributed representations of rooted sub graphs from large graphs"
    - "toward order of magnitude cascade prediction"
    - "line large scale information network embedding"
    - "from micro to macro uncovering and predicting information cascading process with behavioral dynamics"
    - "seismic a self exciting point process model for predicting tweet popularity"

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.SI(Social and Information Networks)