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: "coevolve a joint point process model for information diffusion and network co evolution"
  Abstract: Information diffusion in online social networks is affected by the underlying network topology, but it also has the power to change it. Online users are constantly creating new links when exposed to new information sources, and in turn these links are alternating the way information spreads. However, these two highly intertwined stochastic processes, information diffusion and network evolution, have been predominantly studied separately, ignoring their co-evolutionary dynamics. #R##N#We propose a temporal point process model, COEVOLVE, for such joint dynamics, allowing the intensity of one process to be modulated by that of the other. This model allows us to efficiently simulate interleaved diffusion and network events, and generate traces obeying common diffusion and network patterns observed in real-world networks. Furthermore, we also develop a convex optimization framework to learn the parameters of the model from historical diffusion and network evolution traces. We experimented with both synthetic data and data gathered from Twitter, and show that our model provides a good fit to the data as well as more accurate predictions than alternatives.

Connected through hyperedge HE-1 (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-2 (induced by source paper "self attentive hawkes processes"):
  with members:
    - "self attention with structural position representations"
    - "intensity free learning of temporal point processes"
    - "fully neural network based model for general temporal point processes"
    - "learning temporal point processes via reinforcement learning"
    - "self attention with relative position representations"
    - "benchmarking deep sequential models on volatility predictions for financial time series"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "non local neural networks"

Connected through hyperedge HE-3 (induced by source paper "neural jump stochastic differential equations"):
  with members:
    - "latent odes for irregularly sampled time series"
    - "diffeqflux jl a julia library for neural differential equations"
    - "neural stochastic differential equations deep latent gaussian models in the diffusion limit"
    - "a differentiable programming system to bridge machine learning and scientific computing"
    - "hidden physics models machine learning of nonlinear partial differential equations"
    - "solving for high dimensional committor functions using artificial neural networks"
    - "a comparison of automatic differentiation and continuous sensitivity analysis for derivatives of differential equation solutions"
    - "expecting to be hip hawkes intensity processes for social media popularity"

Connected through hyperedge HE-4 (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"

Connected through hyperedge HE-5 (induced by source paper "using network motifs to characterize temporal network evolution leading to diffusion inhibition"):
  with members:
    - "expecting to be hip hawkes intensity processes for social media popularity"
    - "understanding and forecasting lifecycle events in information cascades"
    - "higher order organization of complex networks"
    - "do cascades recur"
    - "toward early and order of magnitude cascade prediction in social networks"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "can cascades be predicted"
    - "large social networks can be targeted for viral marketing with small seed sets"

Connected through hyperedge HE-6 (induced by source paper "choracle a unified statistical framework for churn prediction"):
  with members:
    - "recurrent poisson factorization for temporal recommendation"
    - "wasserstein learning of deep generative point process models"
    - "tensorflow a system for large scale machine learning"
    - "correlated cascades compete or cooperate"
    - "behavioral modeling for churn prediction early indicators and accurate predictors of custom defection and loyalty"
    - "a recurrent latent variable model for sequential data"
    - "show and tell a neural image caption generator"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-7 (induced by source paper "optimal control of complex systems through variational inference with a discrete event decision process"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "continuous control with deep reinforcement learning"
    - "optimal vaccine allocation to control epidemic outbreaks in arbitrary networks"
    - "belief propagation for structured decision making"
    - "new inference strategies for solving markov decision processes using reversible jump mcmc"
    - "efficient inference in markov control problems"

Connected through hyperedge HE-8 (induced by source paper "learning dynamic embeddings from temporal interactions"):
  with members:
    - "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"
    - "graph embedding techniques applications and performance a survey"
    - "representation learning over dynamic graphs"
    - "predicting interactions between individuals with structural and dynamical information"

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)