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 path integral control"
  Abstract: Recently path integral methods have been developed for stochastic optimal control for a wide class of models with non-linear dynamics in continuous space-time. Path integral methods find the control that minimizes the expected cost-to-go. In this paper we show that under the same assumptions, path integral methods generalize directly to risk sensitive stochastic optimal control. Here the method minimizes in expectation an exponentially weighted cost-to-go. Depending on the exponential weight, risk seeking or risk averse behaviour is obtained. We demonstrate the approach on risk sensitive stochastic optimal control problems beyond the linear-quadratic case, showing the intricate interaction of multi-modal control with risk sensitivity.

Connected through hyperedge HE-1 (induced by source paper "meta learning of sequential strategies"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "on first order meta learning algorithms"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "reptile a scalable metalearning algorithm"
    - "causal reasoning from meta reinforcement learning"
    - "conditional neural processes"
    - "sample efficient adaptive text to speech"
    - "modeling friends and foes"

Connected through hyperedge HE-2 (induced by source paper "an online learning approach to model predictive control"):
  with members:
    - "introduction to online convex optimization"
    - "mirror descent search and its acceleration"
    - "autorally an open platform for aggressive autonomous driving"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "vision based high speed driving with a deep dynamic observer"
    - "acceleration of gradient based path integral method for efficient optimal and inverse optimal control"
    - "adaptive online learning in dynamic environments"
    - "theoretical foundation for cma es from information geometric perspective"

Connected through hyperedge HE-3 (induced by source paper "modeling friends and foes"):
  with members:
    - "the mechanics of n player differentiable games"
    - "what game are we playing end to end learning in normal and extensive form games"
    - "ai safety gridworlds"
    - "the numerics of gans"
    - "concrete problems in ai safety"
    - "an algorithm with nearly optimal pseudo regret for both stochastic and adversarial bandits"
    - "intriguing properties of neural networks"
    - "the best of both worlds stochastic and adversarial bandits"

Connected through hyperedge HE-4 (induced by source paper "ai safety gridworlds"):
  with members:
    - "rainbow combining improvements in deep reinforcement learning"
    - "adversarial attacks on neural network policies"
    - "interactive learning from policy dependent human feedback"
    - "low impact artificial intelligences"
    - "towards proving the adversarial robustness of deep neural networks"
    - "a distributional perspective on reinforcement learning"
    - "enter the matrix safely interruptible autonomous systems via virtualization"
    - "should robots be obedient"

Connected through hyperedge HE-5 (induced by source paper "particle value functions"):
  with members:
    - "particle smoothing for hidden diffusion processes adaptive path integral smoother"
    - "importance weighted autoencoders"
    - "taming the noise in reinforcement learning via soft updates"
    - "risk sensitive reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "planning with information processing constraints and model uncertainty in markov decision processes"):
  with members:
    - "taming the noise in reinforcement learning via soft updates"
    - "risk sensitive and robust decision making a cvar optimization approach"
    - "g learning taming the noise in reinforcement learning via soft updates"
    - "generalized thompson sampling for sequential decision making and causal inference"
    - "risk sensitive reinforcement learning"
    - "efficient bayes adaptive reinforcement learning using sample based search"
    - "a bayesian rule for adaptive control based on causal interventions"
    - "a minimum relative entropy principle for learning and acting"

Connected through hyperedge HE-7 (induced by source paper "an adversarial interpretation of information theoretic bounded rationality"):
  with members:
    - "playing atari with deep reinforcement learning"
    - "information utility bounded rationality"
    - "a monte carlo aixi approximation"

Connected through hyperedge HE-8 (induced by source paper "universal convexification via risk aversion"):
  with members:
    - "a unifying framework for linearly solvable control"
    - "infinite horizon policy gradient estimation"

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)