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: "dyngem deep embedding method for dynamic graphs"
  Abstract: Embedding large graphs in low dimensional spaces has recently attracted significant interest due to its wide applications such as graph visualization, link prediction and node classification. Existing methods focus on computing the embedding for static graphs. However, many graphs in practical applications are dynamic and evolve constantly over time. Naively applying existing embedding algorithms to each snapshot of dynamic graphs independently usually leads to unsatisfactory performance in terms of stability, flexibility and efficiency. In this work, we present an efficient algorithm DynGEM based on recent advances in deep autoencoders for graph embeddings, to address this problem. The major advantages of DynGEM include: (1) the embedding is stable over time, (2) it can handle growing dynamic graphs, and (3) it has better running time than using static embedding methods on each snapshot of a dynamic graph. We test DynGEM on a variety of tasks including graph visualization, graph reconstruction, link prediction and anomaly detection (on both synthetic and real datasets). Experimental results demonstrate the superior stability and scalability of our approach.

Connected through hyperedge HE-1 (induced by source paper "inductive representation learning on temporal graphs"):
  with members:
    - "product knowledge graph embedding for e commerce"
    - "generative graph convolutional network for growing graphs"
    - "fast graph representation learning with pytorch geometric"
    - "dylink2vec effective feature representation for link prediction in dynamic networks"
    - "graph convolutional neural networks for web scale recommender systems"
    - "relational inductive biases deep learning and graph networks"
    - "streaming graph neural networks"
    - "dynamic graph cnn for learning on point clouds"

Connected through hyperedge HE-2 (induced by source paper "dyngraph2vec capturing network dynamics using dynamic graph representation learning"):
  with members:
    - "dylink2vec effective feature representation for link prediction in dynamic networks"
    - "graph embedding techniques applications and performance a survey"
    - "timers error bounded svd restart on dynamic networks"
    - "scalable temporal latent space inference for link prediction in dynamic social networks"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "deep convolutional networks on graph structured data"
    - "line large scale information network embedding"

Connected through hyperedge HE-3 (induced by source paper "temporal attribute prediction via joint modeling of multi relational structure evolution"):
  with members:
    - "evolvegcn evolving graph convolutional networks for dynamic graphs"
    - "recurrent event network for reasoning over temporal knowledge graphs"
    - "learning sequence encoders for temporal knowledge graph completion"
    - "representation learning on graphs methods and applications"
    - "know evolve deep reasoning in temporal knowledge graphs"
    - "convolutional 2d knowledge graph embeddings"
    - "on the properties of neural machine translation encoder decoder approaches"

Connected through hyperedge HE-4 (induced by source paper "regression and singular value decomposition in dynamic graphs"):
  with members:
    - "dyngraph2vec capturing network dynamics using dynamic graph representation learning"
    - "sketch based randomized algorithms for dynamic graph regression"
    - "on the theory of dynamic graph regression problem"
    - "node2vec scalable feature learning for networks"
    - "learning convolutional neural networks for graphs"
    - "powers of tensors and fast matrix multiplication"

Connected through hyperedge HE-5 (induced by source paper "predicting dynamic embedding trajectory in temporal interaction networks"):
  with members:
    - "hierarchical temporal convolutional networks for dynamic recommender systems"
    - "deep learning based recommender system a survey and new perspectives"
    - "dylink2vec effective feature representation for link prediction in dynamic networks"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "leveraging friendship networks for dynamic link prediction in social interaction networks"
    - "community interaction and conflict on the web"
    - "graph embedding techniques applications and performance a survey"
    - "representation learning over dynamic graphs"

Connected through hyperedge HE-6 (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"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "node embedding over temporal graphs"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "graph embedding techniques applications and performance a survey"
    - "representation learning over dynamic graphs"
    - "know evolve deep temporal reasoning for dynamic knowledge graphs"
    - "representation learning on graphs methods and applications"
    - "a survey on network embedding"
    - "attributed network embedding for learning in a dynamic environment"

Connected through hyperedge HE-8 (induced by source paper "dynamic embedding on textual networks via a gaussian process"):
  with members:
    - "graph neural networks for social recommendation"
    - "graph convolutional neural networks for web scale recommender systems"
    - "nscaching simple and efficient negative sampling for knowledge graph embedding"
    - "improved semantic aware network embedding with fine grained word alignment"
    - "diffusion maps for textual network embedding"
    - "spine structural identity preserved inductive network embedding"
    - "representation learning over dynamic graphs"
    - "predicting multicellular function through multi layer tissue 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)