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: "uncovering the temporal dynamics of diffusion networks"
  Abstract: Time plays an essential role in the diffusion of information, influence and disease over networks. In many cases we only observe when a node copies information, makes a decision or becomes infected -- but the connectivity, transmission rates between nodes and transmission sources are unknown. Inferring the underlying dynamics is of outstanding interest since it enables forecasting, influencing and retarding infections, broadly construed. To this end, we model diffusion processes as discrete networks of continuous temporal processes occurring at different rates. Given cascade data -- observed infection times of nodes -- we infer the edges of the global diffusion network and estimate the transmission rates of each edge that best explain the observed data. The optimization problem is convex. The model naturally (without heuristics) imposes sparse solutions and requires no parameter tuning. The problem decouples into a collection of independent smaller problems, thus scaling easily to networks on the order of hundreds of thousands of nodes. Experiments on real and synthetic data show that our algorithm both recovers the edges of diffusion networks and accurately estimates their transmission rates from cascade data.

Connected through hyperedge HE-1 (induced by source paper "deep collaborative embedding for information cascade prediction"):
  with members:
    - "dyngraph2vec capturing network dynamics using dynamic graph representation learning"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "when less is more systematic analysis of cascade based community detection"):
  with members:
    - "systematic analysis of cluster similarity indices towards bias free cluster validation"
    - "community detection through likelihood optimization in search of a sound model"
    - "dani a fast diffusion aware network inference algorithm"
    - "equivalence between modularity optimization and maximum likelihood methods for community detection"
    - "metrics for community analysis a survey"
    - "estimating diffusion network structures recovery conditions sample complexity soft thresholding algorithm"
    - "modeling information propagation with survival theory"
    - "structure and dynamics of information pathways in online media"

Connected through hyperedge HE-3 (induced by source paper "popularity prediction on social platforms with coupled graph neural networks"):
  with members:
    - "graph wavelet neural network"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hierarchical graph representation learning with differentiable pooling"
    - "deepinf social influence prediction with deep learning"
    - "how powerful are graph neural networks"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "expecting to be hip hawkes intensity processes for social media popularity"
    - "neural message passing for quantum chemistry"

Connected through hyperedge HE-4 (induced by source paper "bayesian inference of network structure from information cascades"):
  with members:
    - "super blockers and the effect of network structure on information cascades"
    - "generating connected random graphs"
    - "estimating network structure from unreliable measurements"
    - "dynamic network model from partial observations"
    - "reconstructing networks with unknown and heterogeneous errors"
    - "smerc social media event response clustering using textual and temporal information"
    - "dani a fast diffusion aware network inference algorithm"
    - "the flip markov chain for connected regular graphs"

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"
    - "coevolve a joint point process model for information diffusion and network co evolution"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "can cascades be predicted"

Connected through hyperedge HE-6 (induced by source paper "learning latent process from high dimensional event sequences via efficient sampling"):
  with members:
    - "learning temporal point processes via reinforcement learning"
    - "deep reinforcement learning of marked temporal point processes"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "modeling the intensity function of point process via recurrent neural networks"
    - "wasserstein learning of deep generative point process models"
    - "generative adversarial imitation learning"
    - "learning granger causality for hawkes processes"

Connected through hyperedge HE-7 (induced by source paper "modeling information cascades with self exciting processes via generalized epidemic models"):
  with members:
    - "arming the public with artificial intelligence to counter social bots"
    - "modeling popularity in asynchronous social media streams with recurrent neural networks"
    - "expecting to be hip hawkes intensity processes for social media popularity"
    - "the neural hawkes process a neurally self modulating multivariate point process"
    - "feature driven and point process approaches for popularity prediction"
    - "exploring limits to prediction in complex social systems"
    - "seismic a self exciting point process model for predicting tweet popularity"

Connected through hyperedge HE-8 (induced by source paper "ghostlink latent network inference for influence aware recommendation"):
  with members:
    - "from amateurs to connoisseurs modeling the evolution of user expertise through online reviews"
    - "the author topic model for authors and documents"
    - "information diffusion and external influence in networks"
    - "inferring networks of diffusion and influence"
    - "on the convexity of latent social network inference"

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)