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: "adaptive importance sampling for control and inference"
  Abstract: Path integral (PI) control problems are a restricted class of non-linear control problems that can be solved formally as a Feynman–Kac PI and can be estimated using Monte Carlo sampling. In this contribution we review PI control theory in the finite horizon case. We subsequently focus on the problem how to compute and represent control solutions. We review the most commonly used methods in robotics and control. Within the PI theory, the question of how to compute becomes the question of importance sampling. Efficient importance samplers are state feedback controllers and the use of these requires an efficient representation. Learning and representing effective state-feedback controllers for non-linear stochastic control problems is a very challenging, and largely unsolved, problem. We show how to learn and represent such controllers using ideas from the cross entropy method. We derive a gradient descent method that allows to learn feed-back controllers using an arbitrary parametrisation. We refer to this method as the path integral cross entropy method or PICE. We illustrate this method for some simple examples. The PI control methods can be used to estimate the posterior distribution in latent state models. In neuroscience these problems arise when estimating connectivity from neural recording data using EM. We demonstrate the PI control method as an accurate alternative to particle filtering.

Connected through hyperedge HE-1 (induced by source paper "scalable gradients for stochastic differential equations"):
  with members:
    - "neural sde stabilizing neural ode networks with stochastic noise"
    - "latent odes for irregularly sampled time series"
    - "neural jump stochastic 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"
    - "deep learning with differential gaussian process flows"
    - "stable architectures for deep neural networks"
    - "deep neural networks motivated by partial differential equations"

Connected through hyperedge HE-2 (induced by source paper "probabilistic framework for constrained manipulations and task and motion planning under uncertainty"):
  with members:
    - "describing physics for physical reasoning force based sequential manipulation planning"
    - "optnet differentiable optimization as a layer in neural networks"
    - "a topology guided path integral approach for stochastic optimal control"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-3 (induced by source paper "learning to guide guidance law based on deep meta learning and model predictive path integral control"):
  with members:
    - "adaptive guidance with reinforcement meta learning"
    - "deep haptic model predictive control for robot assisted dressing"
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"
    - "learning to learn how to learn self adaptive visual navigation using meta learning"
    - "learning image conditioned dynamics models for control of under actuated legged millirobots"
    - "data efficient reinforcement learning with probabilistic model predictive control"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "continuous adaptation via meta learning in nonstationary and competitive environments"

Connected through hyperedge HE-4 (induced by source paper "deep value model predictive control"):
  with members:
    - "whole body mpc for a dynamically stable mobile manipulator"
    - "learning agile and dynamic motor skills for legged robots"
    - "temporal difference models model free deep rl for model based control"
    - "real time motion planning of legged robots a model predictive control approach"
    - "an efficient optimal planning and control framework for quadrupedal locomotion"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

Connected through hyperedge HE-5 (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"
    - "particle smoothing for hidden diffusion processes adaptive path integral smoother"

Connected through hyperedge HE-6 (induced by source paper "action selection in growing state spaces control of network structure growth"):
  with members:
    - "coevolve a joint point process model for information diffusion and network co evolution"
    - "shaping social activity by incentivizing users"
    - "a likelihood based framework for the analysis of discussion threads"

Connected through hyperedge HE-7 (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"
    - "particle smoothing for hidden diffusion processes adaptive path integral smoother"
    - "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-8 (induced by source paper "a topology guided path integral approach for stochastic optimal control"):
  with members:
    - "information theoretic stochastic optimal control via incremental sampling based algorithms"
    - "a martingale approach and time consistent sampling based algorithms for risk management in stochastic optimal control"
    - "sampling based algorithms for optimal motion planning"

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.SY(Systems and Control)