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: "dyngraph2vec capturing network dynamics using dynamic graph representation learning"
  Abstract: Abstract   Learning graph representations is a fundamental task aimed at capturing various properties of graphs in vector space. The most recent methods learn such representations for static networks. However, real-world networks evolve over time and have varying dynamics. Capturing such evolution is key to predicting the properties of unseen networks. To understand how the network dynamics affect the prediction performance, we propose an embedding approach which learns the structure of evolution in dynamic graphs and can predict unseen links with higher precision. Our model, dyngraph2vec, learns the temporal transitions in the network using a deep architecture composed of dense and recurrent layers. We motivate the need for capturing dynamics for the prediction on a toy dataset created using stochastic block models. We then demonstrate the efficacy of dyngraph2vec over existing state-of-the-art methods on two real-world datasets. We observe that learning dynamics can improve the quality of embedding and yield better performance in link prediction.

Connected through hyperedge HE-1 (induced by source paper "deep collaborative embedding for information cascade prediction"):
  with members:
    - "iad interaction aware diffusion framework in social networks"
    - "deepinf social influence prediction with deep learning"
    - "attributed social network embedding"
    - "collaborative inference of coexisting information diffusions"
    - "a survey on network embedding"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "topological recurrent neural network for diffusion prediction"
    - "bl mne emerging heterogeneous social network embedding through broad learning with aligned autoencoder"

Connected through hyperedge HE-2 (induced by source paper "exem expert embedding using dominating set theory with deep learning approaches"):
  with members:
    - "relational representation learning for dynamic knowledge graphs a survey"
    - "community aware random walk for network embedding"
    - "dynnode2vec scalable dynamic network embedding"
    - "graph embedding techniques applications and performance a survey"
    - "dynamic graph representation learning via self attention networks"
    - "attributed social network embedding"
    - "network representation learning a survey"
    - "a survey on network embedding"

Connected through hyperedge HE-3 (induced by source paper "regression and singular value decomposition in dynamic graphs"):
  with members:
    - "sketch based randomized algorithms for dynamic graph regression"
    - "on the theory of dynamic graph regression problem"
    - "dyngem deep embedding method for dynamic graphs"
    - "node2vec scalable feature learning for networks"
    - "learning convolutional neural networks for graphs"
    - "powers of tensors and fast matrix multiplication"

Connected through hyperedge HE-4 (induced by source paper "temporal neighbourhood aggregation predicting future links in temporal graphs via recurrent variational graph convolutions"):
  with members:
    - "dynamic graph convolutional networks"
    - "evolvegcn evolving graph convolutional networks for dynamic graphs"
    - "e lstm d a deep learning framework for dynamic network link prediction"
    - "gcn gan a non linear temporal link prediction model for weighted dynamic networks"
    - "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-5 (induced by source paper "dyngraph2seq dynamic graph to sequence interpretable learning for health stage prediction in online health forums"):
  with members:
    - "scalable global alignment graph kernel using random features from node embedding to graph embedding"
    - "sql to text generation with graph to sequence model"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "graph r cnn for scene graph generation"
    - "exploiting rich syntactic information for semantic parsing with graph to sequence model"
    - "learning deep generative models of graphs"
    - "graph2seq graph to sequence learning with attention based neural networks"
    - "representation learning over dynamic graphs"

Connected through hyperedge HE-6 (induced by source paper "variational graph recurrent neural networks"):
  with members:
    - "semi implicit graph variational auto encoders"
    - "dyngem deep embedding method for dynamic graphs"
    - "learning structural node embeddings via diffusion wavelets"
    - "dynamic graph representation learning via self attention networks"
    - "struc2vec learning node representations from structural identity"
    - "timers error bounded svd restart on dynamic networks"
    - "know evolve deep temporal reasoning for dynamic knowledge graphs"
    - "attributed network embedding for learning in a dynamic environment"

Connected through hyperedge HE-7 (induced by source paper "tracking temporal evolution of graphs using non timestamped data"):
  with members:
    - "a comprehensive survey on graph neural networks"
    - "dyngem deep embedding method for dynamic graphs"
    - "lstm a search space odyssey"
    - "timers error bounded svd restart on dynamic networks"
    - "representation learning on graphs methods and applications"
    - "scalable temporal latent space inference for link prediction in dynamic social networks"
    - "youtube 8m a large scale video classification benchmark"
    - "gated feedback recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "networks of power analyzing world leaders interactions on social media"):
  with members:
    - "generalized louvain method for community detection in large networks"
    - "twitter reciprocal reply networks exhibit assortativity with respect to happiness"

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)