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: "feature based classification of networks"
  Abstract: Network representations of systems from various scientific and societal domains are neither completely random nor fully regular, but instead appear to contain recurring structural building blocks. These features tend to be shared by networks belonging to the same broad class, such as the class of social networks or the class of biological networks. At a finer scale of classification within each such class, networks describing more similar systems tend to have more similar features. This occurs presumably because networks representing similar purposes or constructions would be expected to be generated by a shared set of domain specific mechanisms, and it should therefore be possible to classify these networks into categories based on their features at various structural levels. Here we describe and demonstrate a new, hybrid approach that combines manual selection of features of potential interest with existing automated classification methods. In particular, selecting well-known and well-studied features that have been used throughout social network analysis and network science and then classifying with methods such as random forests that are of special utility in the presence of feature collinearity, we find that we achieve higher accuracy, in shorter computation time, with greater interpretability of the network classification results.

Connected through hyperedge HE-1 (induced by source paper "using laplacian spectrum as graph feature representation"):
  with members:
    - "variational recurrent neural networks for graph classification"
    - "a simple baseline algorithm for graph classification"
    - "weighted spectral embedding of graphs"
    - "graph similarity and approximate isomorphism"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "how powerful are graph neural networks"
    - "dynamic routing between capsules"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "variational recurrent neural networks for graph classification"):
  with members:
    - "a simple baseline algorithm for graph classification"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "learning graph level representations with gated recurrent neural networks"
    - "kernel graph convolutional neural networks"
    - "high order stochastic graphlet embedding for graph based pattern recognition"
    - "graph2vec learning distributed representations of graphs"
    - "variational autoencoders for semi supervised text classification"

Connected through hyperedge HE-3 (induced by source paper "unsupervised network embedding for graph visualization clustering and classification"):
  with members:
    - "detecting sequences of system states in temporal networks"
    - "multi hop assortativities for network classification"
    - "fine grained attention mechanism for neural machine translation"
    - "graph embedding techniques applications and performance a survey"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "detecting change points in the large scale structure of evolving networks"

Connected through hyperedge HE-4 (induced by source paper "walk steered convolution for graph classification"):
  with members:
    - "gaussian induced convolution for graphs"
    - "graph embedding techniques applications and performance a survey"
    - "temporal segment networks for action recognition in videos"
    - "representation learning on graphs methods and applications"
    - "shift aggregate extract networks"
    - "robust spatial filtering with graph convolutional neural networks"
    - "learning graph representations with embedding propagation"
    - "gated graph sequence neural networks"

Connected through hyperedge HE-5 (induced by source paper "a simple baseline algorithm for graph classification"):
  with members:
    - "mixed batches and symmetric discriminators for gan training"
    - "weighted spectral embedding of graphs"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "learning graph level representations with gated recurrent neural networks"
    - "kernel graph convolutional neural networks"
    - "high order stochastic graphlet embedding for graph based pattern 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.SI(Social and Information Networks)