human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "topological based classification using graph convolutional networks"):
  Abstract: In colored graphs, node classes are often associated with either their neighbors class or with information not incorporated in the graph associated with each node. We here propose that node classes are also associated with topological features of the nodes. We use this association to improve Graph machine learning in general and specifically, Graph Convolutional Networks (GCN). #R##N#First, we show that even in the absence of any external information on nodes, a good accuracy can be obtained on the prediction of the node class using either topological features, or using the neighbors class as an input to a GCN. This accuracy is slightly less than the one that can be obtained using content based GCN. #R##N#Secondly, we show that explicitly adding the topology as an input to the GCN does not improve the accuracy when combined with external information on nodes. However, adding an additional adjacency matrix with edges between distant nodes with similar topology to the GCN does significantly improve its accuracy, leading to results better than all state of the art methods in multiple datasets.

Includes members:
  Member "gcn gan a non linear temporal link prediction model for weighted dynamic networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fast evaluation of link prediction by random sampling of unobserved links")
  Member "fast interactive object annotation with curve gcn":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep snake for real time instance segmentation")
  Member "regional based query in graph active learning":
    also connected through other hyperedges:
      (none)
  Member "pitfalls of graph neural network evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "benchmarking graph neural networks")
  Member "n gcn multi scale graph convolution for semi supervised node classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "graph inference learning for semi supervised classification")
  Member "semi supervised learning with competitive infection models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "topological based classification of paper domains using graph convolutional networks")
  Member "cayleynets graph convolutional neural networks with complex rational spectral filters":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bridging the gap between spatial and spectral domains a survey on graph neural networks")
  Member "gated graph sequence neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")

Question: Please tell me which class the source paper 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)