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: "learning hawkes processes from short doubly censored event sequences"
  Abstract: Many real-world applications require robust algorithms to learn point processes based on a type of incomplete data --- the so-called short doubly-censored (SDC) event sequences. We study this critical problem of quantitative asynchronous event sequence analysis under the framework of Hawkes processes by leveraging the idea of data synthesis. Given SDC event sequences observed in a variety of time intervals, we propose a sampling-stitching data synthesis method --- sampling predecessors and successors for each SDC event sequence from potential candidates and stitching them together to synthesize long training sequences. The rationality and the feasibility of our method are discussed in terms of arguments based on likelihood. Experiments on both synthetic and real-world data demonstrate that the proposed data synthesis method improves learning results indeed for both time-invariant and time-varying Hawkes processes.

Connected through hyperedge HE-1 (induced by source paper "detection of outlier events in continuous time event sequences"):
  with members:
    - "imputing missing events in continuous time event streams"
    - "the neural hawkes process a neurally self modulating multivariate point process"
    - "learning granger causality for hawkes processes"

Connected through hyperedge HE-2 (induced by source paper "meta learning with relational information for short sequences"):
  with members:
    - "on first order meta learning algorithms"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "reptile a scalable metalearning algorithm"
    - "probabilistic model agnostic meta learning"
    - "motifs in temporal networks"
    - "prototypical networks for few shot learning"
    - "fake news mitigation via point process based intervention"
    - "a dirichlet mixture model of hawkes processes for event sequence clustering"

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 "detecting social influence in event cascades by comparing discriminative rankers"):
  with members:
    - "deepinf social influence prediction with deep learning"
    - "the social dynamics of language change in online networks"
    - "reconstructing parameters of spreading models from partial observations"
    - "node2vec scalable feature learning for networks"
    - "learning granger causality for hawkes processes"
    - "learning influence functions from incomplete observations"
    - "line large scale information network embedding"
    - "seismic a self exciting point process model for predicting tweet popularity"

Connected through hyperedge HE-5 (induced by source paper "contextual outlier detection in continuous time event sequences"):
  with members:
    - "imputing missing events in continuous time event streams"
    - "the neural hawkes process a neurally self modulating multivariate point process"

Connected through hyperedge HE-6 (induced by source paper "discriminative modeling of social influence for prediction and explanation in event cascades"):
  with members:
    - "reconstructing parameters of spreading models from partial observations"
    - "node2vec scalable feature learning for networks"
    - "learning granger causality for hawkes processes"
    - "learning influence functions from incomplete observations"
    - "line large scale information network embedding"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "can cascades be predicted"
    - "is the sample good enough comparing data from twitter s streaming api with twitter s firehose"

Connected through hyperedge HE-7 (induced by source paper "poppy a point process toolbox based on pytorch"):
  with members:
    - "the neural hawkes process a neurally self modulating multivariate point process"
    - "learning granger causality for hawkes processes"
    - "patient flow prediction via discriminative learning of mutually correcting processes"
    - "seismic a self exciting point process model for predicting tweet popularity"

Connected through hyperedge HE-8 (induced by source paper "a dirichlet mixture model of hawkes processes for event sequence clustering"):
  with members:
    - "learning granger causality for hawkes processes"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "shaping social activity by incentivizing users"
    - "modeling events with cascades of poisson processes"

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)