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: "scalable temporal latent space inference for link prediction in dynamic social networks"
  Abstract: We propose a temporal latent space model for link prediction in dynamic social networks, where the goal is to predict links over time based on a sequence of previous graph snapshots. The model assumes that each user lies in an unobserved latent space, and interactions are more likely to occur between similar users in the latent space representation. In addition, the model allows each user to gradually move its position in the latent space as the network structure evolves over time. We present a global optimization algorithm to effectively infer the temporal latent space. Two alternative optimization algorithms with local and incremental updates are also proposed, allowing the model to scale to larger networks without compromising prediction accuracy. Empirically, we demonstrate that our model, when evaluated on a number of real-world dynamic networks, significantly outperforms existing approaches for temporal link prediction in terms of both scalability and predictive power.

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"
    - "timers error bounded svd restart on dynamic 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 "fast evaluation of link prediction by random sampling of unobserved links"):
  with members:
    - "gcn gan a non linear temporal link prediction model for weighted dynamic networks"
    - "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"
    - "performance of local information based link prediction a sampling perspective"

Connected through hyperedge HE-3 (induced by source paper "temporal network representation learning"):
  with members:
    - "heterogeneous network motifs"
    - "node2bits compact time and attribute aware node representations for user stitching"
    - "dylink2vec effective feature representation for link prediction in dynamic networks"
    - "models for capturing temporal smoothness in evolving networks for learning latent representation of nodes"
    - "deep generative dual memory network for continual learning"
    - "attributed social network embedding"
    - "struc2vec learning node representations from structural identity"
    - "psdvec a toolbox for incremental and scalable word embedding"

Connected through hyperedge HE-4 (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"
    - "dyngem deep embedding method for dynamic graphs"
    - "community interaction and conflict on the web"
    - "graph embedding techniques applications and performance a survey"

Connected through hyperedge HE-5 (induced by source paper "node embedding over temporal graphs"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "dyngem deep embedding method for dynamic graphs"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "recurrent event network for reasoning over temporal knowledge graphs"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "evolvegcn evolving graph convolutional networks for dynamic graphs"
    - "predicting dynamic embedding trajectory in temporal interaction networks"
    - "relational representation learning for dynamic knowledge graphs a survey"
    - "hierarchical temporal convolutional networks for dynamic recommender systems"
    - "graph networks as learnable physics engines for inference and control"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "relational inductive biases deep learning and graph networks"

Connected through hyperedge HE-7 (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"
    - "timers error bounded svd restart on dynamic networks"
    - "on the linearity of semantic change investigating meaning variation via dynamic graph models"
    - "adversarial network embedding"

Connected through hyperedge HE-8 (induced by source paper "temporal network embedding with micro and macro dynamics"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "easing embedding learning by comprehensive transcription of heterogeneous information networks"
    - "dyngem deep embedding method for dynamic graphs"
    - "representation learning over dynamic graphs"
    - "a survey on network embedding"
    - "heterogeneous information network embedding for recommendation"
    - "attributed network embedding for learning in a dynamic environment"
    - "a comprehensive survey of graph embedding problems techniques and applications"

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)