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: "timers error bounded svd restart on dynamic networks"
  Abstract: Singular Value Decomposition (SVD) is a popular approach in various network applications, such as link prediction and network parameter characterization. Incremental SVD approaches are proposed to process newly changed nodes and edges in dynamic networks. However, incremental SVD approaches suffer from serious error accumulation inevitably due to approximation on incremental updates. SVD restart is an effective approach to reset the aggregated error, but when to restart SVD for dynamic networks is not addressed in literature. In this paper, we propose TIMERS, Theoretically Instructed Maximum-Error-bounded Restart of SVD, a novel approach which optimally sets the restart time in order to reduce error accumulation in time. Specifically, we monitor the margin between reconstruction loss of incremental updates and the minimum loss in SVD model. To reduce the complexity of monitoring, we theoretically develop a lower bound of SVD minimum loss for dynamic networks and use the bound to replace the minimum loss in monitoring. By setting a maximum tolerated error as a threshold, we can trigger SVD restart automatically when the margin exceeds this threshold.We prove that the time complexity of our method is linear with respect to the number of local dynamic changes, and our method is general across different types of dynamic networks. We conduct extensive experiments on several synthetic and real dynamic networks. The experimental results demonstrate that our proposed method significantly outperforms the existing methods by reducing 27% to 42% in terms of the maximum error for dynamic network reconstruction when fixing the number of restarts. Our method reduces the number of restarts by 25% to 50% when fixing the maximum error tolerated.

Connected through hyperedge HE-1 (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"
    - "dyngem deep embedding method for dynamic graphs"
    - "graph embedding techniques applications and performance a survey"
    - "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-2 (induced by source paper "graphrqi classifying driver behaviors using graph spectrums"):
  with members:
    - "behavior planning of autonomous cars with social perception"
    - "self driving cars a survey"
    - "forecasting trajectory and behavior of road agents using spectral clustering in graph lstms"
    - "roadtrack tracking road agents in dense and heterogeneous environments"
    - "rules of the road predicting driving behavior with a convolutional model of semantic interactions"
    - "densepeds pedestrian tracking in dense crowds using front rvo and sparse features"
    - "intent aware multi agent reinforcement learning"
    - "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"

Connected through hyperedge HE-3 (induced by source paper "dynamic network embeddings for network evolution analysis"):
  with members:
    - "dyngem deep embedding method for dynamic graphs"
    - "dynamic word embeddings for evolving semantic discovery"
    - "diachronic word embeddings and semantic shifts a survey"
    - "struc2vec learning node representations from structural identity"
    - "motifs in temporal networks"
    - "on the linearity of semantic change investigating meaning variation via dynamic graph models"
    - "adversarial network embedding"
    - "scalable temporal latent space inference for link prediction in dynamic social networks"

Connected through hyperedge HE-4 (induced by source paper "variational graph recurrent neural networks"):
  with members:
    - "dyngraph2vec capturing network dynamics using dynamic graph representation learning"
    - "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"
    - "know evolve deep temporal reasoning for dynamic knowledge graphs"
    - "attributed network embedding for learning in a dynamic environment"

Connected through hyperedge HE-5 (induced by source paper "dynwalks global topology and recent changes awareness dynamic network embedding"):
  with members:
    - "dyngem deep embedding method for dynamic graphs"
    - "dynnode2vec scalable dynamic network embedding"
    - "streaming network embedding through local actions"
    - "representation learning on graphs methods and applications"
    - "a survey on network embedding"
    - "scalable temporal latent space inference for link prediction in dynamic social networks"
    - "node2vec scalable feature learning for networks"
    - "line large scale information network embedding"

Connected through hyperedge HE-6 (induced by source paper "tracking temporal evolution of graphs using non timestamped data"):
  with members:
    - "dyngraph2vec capturing network dynamics using dynamic graph representation learning"
    - "a comprehensive survey on graph neural networks"
    - "dyngem deep embedding method for dynamic graphs"
    - "lstm a search space odyssey"
    - "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-7 (induced by source paper "dna dynamic social network alignment"):
  with members:
    - "unsupervised user identity linkage via factoid embedding"
    - "meta diagram based active social networks alignment"
    - "adversarially regularized graph autoencoder for graph embedding"
    - "network representation learning a survey"
    - "a survey on network embedding"
    - "alignment of dynamic networks"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "time aware gradient attack on dynamic network link prediction"):
  with members:
    - "exact k recommendation via maximal clique optimization"
    - "e lstm d a deep learning framework for dynamic network link prediction"
    - "attack graph convolutional networks by adding fake nodes"
    - "data poisoning attack against unsupervised node embedding methods"
    - "hiding individuals and communities in a social network"
    - "semi supervised classification with graph convolutional networks"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"

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)