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 kullback leibler control for continuous state systems using probabilistic graphical models"
  Abstract: Kullback Leibler (KL) control problems allow for efficient computation of optimal control by solving a principal eigenvector problem. However, direct applicability of such framework to continuous state-action systems is limited. In this paper, we propose to embed a KL control problem in a probabilistic graphical model where observed variables correspond to the continuous (possibly high-dimensional) state of the system and latent variables correspond to a discrete (low-dimensional) representation of the state amenable for KL control computation. We present two examples of this approach. The first one uses standard hidden Markov models (HMMs) and computes exact optimal control, but is only applicable to low-dimensional systems. The second one uses factorial HMMs, it is scalable to higher dimensional problems, but control computation is approximate. We illustrate both examples in several robot motor control tasks.

Connected through hyperedge HE-1 (induced by source paper "fast rates for online learning in linearly solvable markov decision processes"):
  with members:
    - "introduction to online convex optimization"
    - "action selection in growing state spaces control of network structure growth"
    - "fast rates in statistical and online learning"
    - "real time stochastic optimal control for multi agent quadrotor systems"
    - "regal a regularization based algorithm for reinforcement learning in weakly communicating mdps"

Connected through hyperedge HE-2 (induced by source paper "hierarchical linearly solvable markov decision problems"):
  with members:
    - "hierarchical reinforcement learning with the maxq value function decomposition"
    - "state abstraction in maxq hierarchical reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "embed to control a locally linear latent dynamics model for control from raw images"):
  with members:
    - "draw a recurrent neural network for image generation"
    - "end to end training of deep visuomotor policies"
    - "adam a method for stochastic optimization"
    - "nice non linear independent components estimation"
    - "learning to generate chairs with convolutional neural networks"
    - "transformation properties of learned visual representations"
    - "exact solutions to the nonlinear dynamics of learning in deep linear neural networks"
    - "deep autoregressive networks"

Connected through hyperedge HE-4 (induced by source paper "real time stochastic optimal control for multi agent quadrotor systems"):
  with members:
    - "gpu based path integral control with learned dynamics"
    - "outdoor flocking and formation flight with autonomous aerial robots"
    - "probabilistic planning for continuous dynamic systems under bounded risk"
    - "path integral control by reproducing kernel hilbert space embedding"
    - "path integral policy improvement with covariance matrix adaptation"

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)