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: "safe exploration in finite markov decision processes with gaussian processes"
  Abstract: In classical reinforcement learning, when exploring an environment, agents accept arbitrary short term loss for long term gain. This is infeasible for safety critical applications, such as robotics, where even a single unsafe action may cause system failure. In this paper, we address the problem of safely exploring finite Markov decision processes (MDP). We define safety in terms of an, a priori unknown, safety constraint that depends on states and actions. We aim to explore the MDP under this constraint, assuming that the unknown function satisfies regularity conditions expressed via a Gaussian process prior. We develop a novel algorithm for this task and prove that it is able to completely explore the safely reachable part of the MDP without violating the safety constraint. To achieve this, it cautiously explores safe states and actions in order to gain statistical confidence about the safety of unvisited state-action pairs from noisy observations collected while navigating the environment. Moreover, the algorithm explicitly considers reachability when exploring the MDP, ensuring that it does not get stuck in any state with no safe way out. We demonstrate our method on digital terrain models for the task of exploring an unknown map with a rover.

Connected through hyperedge HE-1 (induced by source paper "cautious reinforcement learning with logical constraints"):
  with members:
    - "certified reinforcement learning with logic guidance"
    - "rethinking the discount factor in reinforcement learning a decision theoretic approach"
    - "reinforcement learning for temporal logic control synthesis with probabilistic satisfaction guarantees"
    - "modular deep reinforcement learning with temporal logic specifications"
    - "verifiably safe off model reinforcement learning"
    - "logically constrained reinforcement learning"
    - "logically constrained neural fitted q iteration"
    - "shielded decision making in mdps"

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 "convergent policy optimization for safe reinforcement learning"):
  with members:
    - "neural proximal trust region policy optimization attains globally optimal policy"
    - "provably efficient q learning with low switching cost"
    - "on the global convergence of actor critic a case for linear quadratic regulator with ergodic cost"
    - "neural temporal difference learning converges to global optima"
    - "neural policy gradient methods global optimality and rates of convergence"
    - "stochastic successive convex approximation for non convex constrained stochastic optimization"
    - "learning safe policies with expert guidance"
    - "communication efficient distributed reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "robust regression for safe exploration in control"):
  with members:
    - "conditioning by adaptive sampling for robust design"
    - "episodic learning with control lyapunov functions for uncertain robotic systems"
    - "control regularization for reduced variance reinforcement learning"
    - "end to end safe reinforcement learning through barrier functions for safety critical continuous control tasks"
    - "stagewise safe bayesian optimization with gaussian processes"
    - "neural lander stable drone landing control using learned dynamics"
    - "spectral normalization for generative adversarial networks"
    - "measuring and regularizing networks in function space"

Connected through hyperedge HE-5 (induced by source paper "an inductive synthesis framework for verifiable reinforcement learning"):
  with members:
    - "concolic testing for deep neural networks"
    - "programmatically interpretable reinforcement learning"
    - "reachability analysis of deep neural networks with provable guarantees"
    - "verifiable reinforcement learning via policy extraction"
    - "feature guided black box safety testing of deep neural networks"
    - "deep reinforcement learning an overview"
    - "towards generalization and simplicity in continuous control"
    - "reluplex an efficient smt solver for verifying deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "dependable neural networks for safety critical tasks"):
  with members:
    - "compound domain adaptation in an open world"
    - "a survey of deep learning techniques for autonomous driving"
    - "automatic adaptation of object detectors to new domains using self training"
    - "towards physically safe reinforcement learning under supervision"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "do cifar 10 classifiers generalize to cifar 10"
    - "task embedded control networks for few shot imitation learning"
    - "rigorous agent evaluation an adversarial approach to uncover catastrophic failures"

Connected through hyperedge HE-7 (induced by source paper "safe reinforcement learning via online shielding"):
  with members:
    - "verisig verifying safety properties of hybrid systems with neural network controllers"
    - "a lyapunov based approach to safe reinforcement learning"
    - "programmatically interpretable reinforcement learning"
    - "verifiable reinforcement learning via policy extraction"
    - "constrained policy optimization"
    - "safe reinforcement learning via shielding"
    - "concrete problems in ai safety"
    - "end to end training of deep visuomotor policies"

Connected through hyperedge HE-8 (induced by source paper "a learnable safety measure"):
  with members:
    - "learning agile and dynamic motor skills for legged robots"
    - "feedback control for cassie with deep reinforcement learning"
    - "real time control for autonomous racing based on viability theory"
    - "a general safety framework for learning based control in uncertain robotic systems"
    - "bayesian optimization using domain knowledge on the atrias biped"
    - "hamilton jacobi reachability a brief overview and recent advances"
    - "safe controller optimization for quadrotors with gaussian processes"
    - "safe exploration in markov decision processes"

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)