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: "risk sensitive nonlinear optimal control with measurement uncertainty"
  Abstract: We present an algorithm to synthesize locally-optimal feedback controllers that take into account additive process and measurement noise for nonlinear stochastic optimal control problems. The algorithm is based on an exponential performance criteria which allows to optimize not only the expected value of the cost, but also a linear combination of its higher order moments; thereby, the cost of uncertainty can be taken into account for synthesis of robust or risk-sensitive policies. The method constructs an affine feedback control law, whose gains explicitly depend upon the covariance of the estimation errors and process noise. Despite the fact that controller and observer are designed separately, the measurement noise variance enters the optimal control, therefore generating feedback laws that do not rely on the Certainty Equivalence Principle. The capabilities of the approach are illustrated in simulation on a two degree of freedom (DOF) manipulator, first in a waypoint task and then in a task where the manipulator goes in contact with its environment.

Connected through hyperedge HE-1 (induced by source paper "curious ilqr resolving uncertainty in model based rl"):
  with members:
    - "intrinsic motivation and mental replay enable efficient online adaptation in stochastic recurrent networks"
    - "model based active exploration"
    - "curiosity driven exploration of learned disentangled goal spaces"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "curiosity driven exploration by self supervised prediction"
    - "unifying count based exploration and intrinsic motivation"
    - "risk sensitive nonlinear optimal control iterative linear exponential quadratic optimal control with gaussian noise"

Connected through hyperedge HE-2 (induced by source paper "a family of iterative gauss newton shooting methods for nonlinear optimal control"):
  with members:
    - "the control toolbox an open source c library for robotics optimal and model predictive control"
    - "whole body nonlinear model predictive control through contacts for quadrupeds"
    - "efficient kinematic planning for mobile manipulators with non holonomic constraints using optimal control"
    - "an efficient optimal planning and control framework for quadrupedal locomotion"
    - "automatic differentiation of rigid body dynamics for optimal control and estimation"
    - "trajectory optimization through contacts and automatic gait discovery for quadrupeds"

Connected through hyperedge HE-3 (induced by source paper "balancing and walking using full dynamics lqr control with contact constraints"):
  with members:
    - "robot impedance control and passivity analysis with inner torque and velocity feedback loops"
    - "inertial sensor based humanoid joint state estimation"
    - "balancing experiments on a torque controlled humanoid with hierarchical inverse dynamics"

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)