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 continuous action spaces"
  Abstract: We address the problem of deploying a reinforcement learning (RL) agent on a physical system such as a datacenter cooling unit or robot, where critical constraints must never be violated. We show how to exploit the typically smooth dynamics of these systems and enable RL algorithms to never violate constraints during learning. Our technique is to directly add to the policy a safety layer that analytically solves an action correction formulation per each state. The novelty of obtaining an elegant closed-form solution is attained due to a linearized model, learned on past trajectories consisting of arbitrary actions. This is to mimic the real-world circumstances where data logs were generated with a behavior policy that is implausible to describe mathematically; such cases render the known safety-aware off-policy methods inapplicable. We demonstrate the efficacy of our approach on new representative physics-based environments, and prevail where reward shaping fails by maintaining zero constraint violations.

Connected through hyperedge HE-1 (induced by source paper "learning to walk in the real world with minimal human effort"):
  with members:
    - "safety guided deep reinforcement learning via online gaussian process estimation"
    - "solving rubik s cube with a robot hand"
    - "generalizing from a few environments in safety critical reinforcement learning"
    - "data efficient reinforcement learning for legged robots"
    - "deepgait planning and control of quadrupedal gaits using deep reinforcement learning"
    - "norml no reward meta learning"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "learning agile and dynamic motor skills for legged robots"

Connected through hyperedge HE-2 (induced by source paper "striving for simplicity and performance in off policy drl output normalization and non uniform sampling"):
  with members:
    - "diagnosing bottlenecks in deep q learning algorithms"
    - "benchmarking model based reinforcement learning"
    - "remember and forget for experience replay"
    - "supervised policy update for deep reinforcement learning"
    - "clipped action policy gradient"
    - "marginal policy gradients a unified family of estimators for bounded action spaces with applications"
    - "curiosity driven experience prioritization via density estimation"
    - "addressing function approximation error in actor critic methods"

Connected through hyperedge HE-3 (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-4 (induced by source paper "imitation projected programmatic reinforcement learning"):
  with members:
    - "batch policy learning under constraints"
    - "control regularization for reduced variance reinforcement learning"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "houdini lifelong learning as program synthesis"
    - "predictor corrector policy optimization"
    - "programmatically interpretable reinforcement learning"
    - "accelerating imitation learning with predictive models"
    - "verifiable reinforcement learning via policy extraction"

Connected through hyperedge HE-5 (induced by source paper "towards simplicity in deep reinforcement learning streamlined off policy learning"):
  with members:
    - "diagnosing bottlenecks in deep q learning algorithms"
    - "benchmarking model based reinforcement learning"
    - "remember and forget for experience replay"
    - "supervised policy update for deep reinforcement learning"
    - "clipped action policy gradient"
    - "marginal policy gradients a unified family of estimators for bounded action spaces with applications"
    - "curiosity driven experience prioritization via density estimation"
    - "soft actor critic algorithms and applications"

Connected through hyperedge HE-6 (induced by source paper "imitation projected policy gradient for programmatic reinforcement learning"):
  with members:
    - "control regularization for reduced variance reinforcement learning"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "dual policy iteration"
    - "houdini lifelong learning as program synthesis"
    - "predictor corrector policy optimization"
    - "programmatically interpretable reinforcement learning"
    - "accelerating imitation learning with predictive models"
    - "fast policy learning through imitation and reinforcement"

Connected through hyperedge HE-7 (induced by source paper "temporal logic guided safe reinforcement learning using control barrier functions"):
  with members:
    - "barrier certified adaptive reinforcement learning with applications to brushbot navigation"
    - "end to end safe reinforcement learning through barrier functions for safety critical continuous control tasks"
    - "automata guided reinforcement learning with demonstrations"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "the surprising creativity of digital evolution a collection of anecdotes from the evolutionary computation and artificial life research communities"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"
    - "reinforcement learning for ltlf ldlf goals"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"

Connected through hyperedge HE-8 (induced by source paper "learning human objectives by evaluating hypothetical behavior"):
  with members:
    - "improving safety in reinforcement learning using model based architectures and human intervention"
    - "model based reinforcement learning for atari"
    - "risk aware active inverse reinforcement learning"
    - "learning latent dynamics for planning from pixels"
    - "supervising strong learners by amplifying weak experts"
    - "scalable agent alignment via reward modeling a research direction"
    - "active inverse reward design"
    - "reward learning from human preferences and demonstrations in atari"

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.AI(Artificial Intelligence)