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: "policy gradients with variance related risk criteria"
  Abstract: Managing risk in dynamic decision problems is of cardinal importance in many fields such as finance and process control. The most common approach to defining risk is through various variance related criteria such as the Sharpe Ratio or the standard deviation adjusted reward. It is known that optimizing many of the variance related risk criteria is NP-hard. In this paper we devise a framework for local policy gradient style algorithms for reinforcement learning for variance related criteria. Our starting point is a new formula for the variance of the cost-to-go in episodic tasks. Using this formula we develop policy gradient algorithms for criteria that involve both the expected cost and the variance of the cost. We prove the convergence of these algorithms to local minima and demonstrate their applicability in a portfolio planning problem.

Connected through hyperedge HE-1 (induced by source paper "safe reinforcement learning for autonomous vehicles through parallel constrained policy optimization"):
  with members:
    - "addressing value estimation errors in reinforcement learning with a state action return distribution function"
    - "deep reinforcement learning for autonomous driving"
    - "virtual to real reinforcement learning for autonomous driving"
    - "constrained policy optimization"
    - "safe reinforcement learning via shielding"
    - "continuous control with deep reinforcement learning"
    - "trust region policy optimization"
    - "safe exploration of state and action spaces in reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "challenges of real world reinforcement learning"):
  with members:
    - "value constrained model free continuous control"
    - "deepmind control suite"
    - "learning latent dynamics for planning from pixels"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "optimizing agent behavior over long time scales by transporting value"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "reward constrained policy optimization"

Connected through hyperedge HE-3 (induced by source paper "robust reinforcement learning for continuous control with model misspecification"):
  with members:
    - "challenges of real world reinforcement learning"
    - "a bayesian approach to robust reinforcement learning"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "deepmind control suite"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "soft actor critic algorithms and applications"
    - "unicorn continual learning with a universal off policy agent"

Connected through hyperedge HE-4 (induced by source paper "td regularized actor critic methods"):
  with members:
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "rainbow combining improvements in deep reinforcement learning"
    - "deep reinforcement learning that matters"
    - "f divergence constrained policy improvement"
    - "trust pcl an off policy trust region method for continuous control"
    - "boosting the actor with dual critic"

Connected through hyperedge HE-5 (induced by source paper "don t forget your teacher a corrective reinforcement learning framework"):
  with members:
    - "breaking the curse of horizon infinite horizon off policy estimation"
    - "a lyapunov based approach to safe reinforcement learning"
    - "programmatically interpretable reinforcement learning"
    - "kickstarting deep reinforcement learning"
    - "ai safety gridworlds"
    - "divide and conquer reinforcement learning"
    - "constrained policy optimization"
    - "a deep q network for the beer game a deep reinforcement learning algorithm to solve inventory optimization problems"

Connected through hyperedge HE-6 (induced by source paper "worst cases policy gradients"):
  with members:
    - "implicit quantile networks for distributional reinforcement learning"
    - "distributed distributional deterministic policy gradients"
    - "rainbow combining improvements in deep reinforcement learning"
    - "robust adversarial reinforcement learning"
    - "carla an open urban driving simulator"
    - "a distributional perspective on reinforcement learning"
    - "on a formal model of safe and scalable self driving cars"
    - "distributional reinforcement learning with quantile regression"

Connected through hyperedge HE-7 (induced by source paper "formal language constraints for markov decision processes"):
  with members:
    - "automata guided reinforcement learning with demonstrations"
    - "logically constrained neural fitted q iteration"
    - "shielded decision making in mdps"
    - "verifiable reinforcement learning via policy extraction"
    - "environment independent task specifications via gltl"
    - "constrained policy optimization"
    - "safe reinforcement learning via shielding"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-8 (induced by source paper "safety guided deep reinforcement learning via online gaussian process estimation"):
  with members:
    - "a lyapunov based approach to safe reinforcement learning"
    - "ai safety gridworlds"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"
    - "leave no trace learning to reset for safe and autonomous reinforcement learning"
    - "constrained policy optimization"
    - "on kernelized multi armed bandits"
    - "deep q learning from demonstrations"
    - "safe reinforcement learning via shielding"

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)