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: "gcn gan a non linear temporal link prediction model for weighted dynamic networks"
  Abstract: In this paper, we generally formulate the dynamics prediction problem of various network systems (e.g., the prediction of mobility, traffic and topology) as the temporal link prediction task. Different from conventional techniques of temporal link prediction that ignore the potential non-linear characteristics and the informative link weights in the dynamic network, we introduce a novel non-linear model GCN-GAN to tackle the challenging temporal link prediction task of weighted dynamic networks. The proposed model leverages the benefits of the graph convolutional network (GCN), long short-term memory (LSTM) as well as the generative adversarial network (GAN). Thus, the dynamics, topology structure and evolutionary patterns of weighted dynamic networks can be fully exploited to improve the temporal link prediction performance. Concretely, we first utilize GCN to explore the local topological characteristics of each single snapshot and then employ LSTM to characterize the evolving features of the dynamic networks. Moreover, GAN is used to enhance the ability of the model to generate the next weighted network snapshot, which can effectively tackle the sparsity and the wide-value-range problem of edge weights in real-life dynamic networks. To verify the model's effectiveness, we conduct extensive experiments on four datasets of different network systems and application scenarios. The experimental results demonstrate that our model achieves impressive results compared to the state-of-the-art competitors.

Connected through hyperedge HE-1 (induced by source paper "fast evaluation of link prediction by random sampling of unobserved links"):
  with members:
    - "link prediction based on graph neural networks"
    - "link prediction in dynamic graphs for recommendation"
    - "multi task graph autoencoders"
    - "modeling polypharmacy side effects with graph convolutional networks"
    - "end to end structure aware convolutional networks for knowledge base completion"
    - "gemsec graph embedding with self clustering"
    - "scalable temporal latent space inference for link prediction in dynamic social networks"
    - "performance of local information based link prediction a sampling perspective"

Connected through hyperedge HE-2 (induced by source paper "temporal neighbourhood aggregation predicting future links in temporal graphs via recurrent variational graph convolutions"):
  with members:
    - "dyngraph2vec capturing network dynamics using dynamic graph representation learning"
    - "dynamic graph convolutional networks"
    - "evolvegcn evolving graph convolutional networks for dynamic graphs"
    - "e lstm d a deep learning framework for dynamic network link prediction"
    - "pytorch an imperative style high performance deep learning library"
    - "temporal graph offset reconstruction towards temporally robust graph representation learning"
    - "gc lstm graph convolution embedded lstm for dynamic link prediction"
    - "dynamicgem a library for dynamic graph embedding methods"

Connected through hyperedge HE-3 (induced by source paper "topological based classification using graph convolutional networks"):
  with members:
    - "fast interactive object annotation with curve gcn"
    - "regional based query in graph active learning"
    - "pitfalls of graph neural network evaluation"
    - "n gcn multi scale graph convolution for semi supervised node classification"
    - "semi supervised learning with competitive infection models"
    - "cayleynets graph convolutional neural networks with complex rational spectral filters"
    - "gated graph sequence neural networks"
    - "node2vec scalable feature learning for networks"

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)