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: "latent self exciting point process model for spatial temporal networks"
  Abstract: We  propose a latent self-exciting point process model that describes geographically distributed interactions between pairs of entities. In contrast to most existing approaches that assume fully observable interactions, here we consider a scenario where certain interaction events lack information about participants. Instead, this information needs to be inferred from the available observations. We develop an efficient approximate algorithm based on variational expectation-maximization to infer unknown participants in an event given the location and the time of the event. We validate the model on synthetic as well as real-world data, and obtain very promising results on the identity-inference task. We also use our model to predict the timing and participants of future events, and demonstrate that it compares favorably with baseline approaches.

Connected through hyperedge HE-1 (induced by source paper "diffusion on dynamic contact networks with indirect transmission links"):
  with members:
    - "lightweight mobile ad hoc network routing protocols for smartphones"
    - "impact of indirect contacts in emerging infectious disease on social networks"
    - "least cost influence maximization across multiple social networks"
    - "diffusion in colocation contact networks the impact of nodal spatiotemporal dynamics"
    - "an introduction to temporal graphs an algorithmic perspective"
    - "common neighbours and the local community paradigm for topological link prediction in bipartite networks"
    - "time varying graphs and dynamic networks"
    - "information propagation speed in mobile and delay tolerant networks"

Connected through hyperedge HE-2 (induced by source paper "multivariate spatiotemporal hawkes processes and network reconstruction"):
  with members:
    - "inferring multiplex diffusion network via multivariate marked hawkes process"
    - "graph based deep modeling and real time forecasting of sparse spatio temporal data"
    - "the role of network analysis in industrial and applied mathematics a physical applied mathematics perspective"
    - "crime topic modeling"
    - "the geospatial characteristics of a social movement communication network"
    - "modeling events with cascades of poisson processes"
    - "the anatomy of the facebook social graph"

Connected through hyperedge HE-3 (induced by source paper "spatio temporal modeling of users check ins in location based social networks"):
  with members:
    - "back to the past source identification in diffusion networks from partially observed cascades"
    - "correlated cascades compete or cooperate"
    - "modeling adoption and usage of competing products"
    - "modeling information propagation with survival theory"

Connected through hyperedge HE-4 (induced by source paper "understanding the predictive power of computational mechanics and echo state networks in social media"):
  with members:
    - "blind construction of optimal nonlinear recursive predictors for discrete sequences"
    - "collective phenomena and non finite state computation in a human social system"
    - "information transfer in social media"

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)