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: "reward constrained policy optimization"
  Abstract: Solving tasks in Reinforcement Learning is no easy feat. As the goal of the agent is to maximize the accumulated reward, it often learns to exploit loopholes and misspecifications in the reward signal resulting in unwanted behavior. While constraints may solve this issue, there is no closed form solution for general constraints. In this work we present a novel multi-timescale approach for constrained policy optimization, called `Reward Constrained Policy Optimization' (RCPO), which uses an alternative penalty signal to guide the policy towards a constraint satisfying one. We prove the convergence of our approach and provide empirical evidence of its ability to train constraint satisfying policies.

Connected through hyperedge HE-1 (induced by source paper "constrained deep reinforcement learning for energy sustainable multi uav based random access iot networks with noma"):
  with members:
    - "optimizing non orthogonal multiple access in random access networks"
    - "deep reinforcement learning for minimizing age of information in uav assisted networks"
    - "a game theoretic approach for noma aloha"
    - "optimal 3d trajectory design and resource allocation for solar powered uav communication systems"
    - "risk sensitive reinforcement learning a constrained optimization viewpoint"
    - "addressing function approximation error in actor critic methods"
    - "accelerated primal dual policy optimization for safe reinforcement learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-2 (induced by source paper "exploration exploitation in constrained mdps"):
  with members:
    - "conservative exploration in reinforcement learning"
    - "improved algorithms for conservative exploration in bandits"
    - "optimistic policy optimization with bandit feedback"
    - "constrained upper confidence reinforcement learning"
    - "a modern introduction to online learning"
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "online convex optimization in adversarial markov decision processes"
    - "provably efficient exploration in policy optimization"

Connected through hyperedge HE-3 (induced by source paper "first order optimization in policy space for constrained deep reinforcement learning"):
  with members:
    - "lyapunov based safe policy optimization for continuous control"
    - "supervised policy update for deep reinforcement learning"
    - "deep reinforcement learning that matters"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "constrained policy optimization"
    - "proximal policy optimization algorithms"
    - "concrete problems in ai safety"

Connected through hyperedge HE-4 (induced by source paper "reward free exploration for reinforcement learning"):
  with members:
    - "provably efficient rl with rich observations via latent state decoding"
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "reinforcement learning with convex constraints"
    - "information theoretic considerations in batch reinforcement learning"
    - "provably efficient exploration in policy optimization"
    - "kinematic state abstraction and provably efficient rich observation reinforcement learning"
    - "provably efficient maximum entropy exploration"

Connected through hyperedge HE-5 (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"
    - "safe exploration in continuous action spaces"

Connected through hyperedge HE-6 (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-7 (induced by source paper "learning representations in reinforcement learning an information bottleneck approach"):
  with members:
    - "a geometric perspective on optimal representations for reinforcement learning"
    - "learning latent dynamics for planning from pixels"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "state representation learning for control an overview"
    - "addressing function approximation error in actor critic methods"
    - "mine mutual information neural estimation"
    - "variational discriminator bottleneck improving imitation learning inverse rl and gans by constraining information flow"

Connected through hyperedge HE-8 (induced by source paper "neural simplex architecture"):
  with members:
    - "safety aware adaptive reinforcement learning with applications to brushbot navigation"
    - "a lyapunov based approach to safe reinforcement learning"
    - "verification for machine learning autonomy and neural networks survey"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "constrained policy optimization"
    - "safe reinforcement learning via shielding"
    - "proximal policy optimization algorithms"

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)