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: "particle smoothing for hidden diffusion processes adaptive path integral smoother"
  Abstract: Smoothing methods are used for inference of stochastic processes given noisy observations. The estimation of the marginal posterior distribution given all observations is typically a computationally intensive task. We propose a novel algorithm based on path integral control theory to efficiently estimate the smoothing distribution of continuous-time diffusion processes from partial observations. In particular, we use an adaptive importance sampling method to improve the effective sampling size of the posterior and the reliability of the estimation of the marginals. This is achieved by estimating a feedback controller, together with an adaptive initialization and an annealing scheme to sample efficiently from the joint smoothing distribution. We compare the results with estimations obtained from the standard Forward Filter/Backward Simulator (FFBSi) for two diffusion processes of different complexity. We show that the proposed method gives more accurate estimates than the standard FFBSi.

Connected through hyperedge HE-1 (induced by source paper "adaptive path integral autoencoder representation learning and planning for dynamical systems"):
  with members:
    - "deepmind control suite"
    - "state representation learning for control an overview"
    - "learning implicit sampling distributions for motion planning"
    - "inference suboptimality in variational autoencoders"
    - "approximate inference based motion planning by learning and exploiting low dimensional latent variable models"
    - "filtering variational objectives"
    - "learning sampling distributions for robot motion planning"
    - "path integral networks end to end differentiable optimal control"

Connected through hyperedge HE-2 (induced by source paper "particle value functions"):
  with members:
    - "importance weighted autoencoders"
    - "taming the noise in reinforcement learning via soft updates"
    - "risk sensitive reinforcement learning"
    - "risk sensitive path integral control"

Connected through hyperedge HE-3 (induced by source paper "topology guided path integral approach for stochastic optimal control in cluttered environment"):
  with members:
    - "adaptive path integral autoencoder representation learning and planning for dynamical systems"
    - "adaptive importance sampling for control and inference"
    - "a topology guided path integral approach for stochastic optimal control"
    - "model predictive path integral control using covariance variable importance sampling"
    - "information theoretic stochastic optimal control via incremental sampling based algorithms"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-4 (induced by source paper "adaptive importance sampling for control and inference"):
  with members:
    - "real time stochastic optimal control for multi agent quadrotor systems"

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)