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: "modeling events with cascades of poisson processes"
  Abstract: We present a probabilistic model of events in continuous time in which each event triggers a Poisson process of successor events. The ensemble of observed events is thereby modeled as a superposition of Poisson processes. Efficient inference is feasible under this model with an EM algorithm. Moreover, the EM algorithm can be implemented as a distributed algorithm, permitting the model to be applied to very large datasets. We apply these techniques to the modeling of Twitter messages and the revision history of Wikipedia.

Connected through hyperedge HE-1 (induced by source paper "a multi channel neural graphical event model with negative evidence"):
  with members:
    - "time dependent representation for neural event sequence prediction"
    - "modeling the intensity function of point process via recurrent neural networks"
    - "wasserstein learning of deep generative point process models"
    - "the neural hawkes process a neurally self modulating multivariate point process"
    - "ct nor representing and reasoning about events in continuous time"

Connected through hyperedge HE-2 (induced by source paper "consistent community detection in continuous time networks of relational events"):
  with members:
    - "expecting to be hip hawkes intensity processes for social media popularity"
    - "the block point process model for continuous time event based dynamic networks"
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "stochastic block transition models for dynamic networks"

Connected through hyperedge HE-3 (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"
    - "latent self exciting point process model for spatial temporal networks"
    - "the geospatial characteristics of a social movement communication network"
    - "the anatomy of the facebook social graph"

Connected through hyperedge HE-4 (induced by source paper "debatenight the role and influence of socialbots on twitter during the 1st u s presidential debate"):
  with members:
    - "expecting to be hip hawkes intensity processes for social media popularity"
    - "online human bot interactions detection estimation and characterization"
    - "botornot a system to evaluate social bots"
    - "feature driven and point process approaches for popularity prediction"
    - "uncovering the temporal dynamics of diffusion networks"
    - "novelty and collective attention"

Connected through hyperedge HE-5 (induced by source paper "personalized thread recommendation for mooc discussion forums"):
  with members:
    - "a continuous time mutually exciting point process framework for prioritizing events in social media"
    - "communication communities in moocs"
    - "learning about social learning in moocs from statistical analysis to generative model"

Connected through hyperedge HE-6 (induced by source paper "expecting to be hip hawkes intensity processes for social media popularity"):
  with members:
    - "tideh time dependent hawkes process for predicting retweet dynamics"
    - "feature driven and point process approaches for popularity prediction"
    - "do cascades recur"
    - "exploring limits to prediction in complex social systems"
    - "modeling and predicting popularity dynamics of microblogs using self excited hawkes processes"
    - "learning and forecasting opinion dynamics in social networks"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "shaping social activity by incentivizing users"

Connected through hyperedge HE-7 (induced by source paper "fake news mitigation via point process based intervention"):
  with members:
    - "recurrent poisson factorization for temporal recommendation"
    - "distilling information reliability and source trustworthiness from digital traces"
    - "wasserstein learning of deep generative point process models"
    - "multistage campaigning in social networks"
    - "steering opinion dynamics in information diffusion networks"
    - "detecting weak changes in dynamic events over networks"
    - "learning granger causality for hawkes processes"
    - "smart broadcasting do you want to be seen"

Connected through hyperedge HE-8 (induced by source paper "recurrent poisson factorization for temporal recommendation"):
  with members:
    - "continuous time user modeling in the presence of badges a probabilistic approach"
    - "spatio temporal modeling of users check ins in location based social networks"
    - "correlated cascades compete or cooperate"
    - "dynamic poisson factorization"
    - "modeling adoption and usage of competing products"

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.LG(Machine Learning)