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: "optlayer practical constrained optimization for deep reinforcement learning in the real world"
  Abstract: While deep reinforcement learning techniques have recently produced considerable achievements on many decision-making problems, their use in robotics has largely been limited to simulated worlds or restricted motions, since unconstrained trial-and-error interactions in the real world can have undesirable consequences for the robot or its environment. To overcome such limitations, we propose a novel reinforcement learning architecture, OptLayer, that takes as inputs possibly unsafe actions predicted by a neural network and outputs the closest actions that satisfy chosen constraints. While learning control policies often requires carefully crafted rewards and penalties while exploring the range of possible actions, OptLayer ensures that only safe actions are actually executed and unsafe predictions are penalized during training. We demonstrate the effectiveness of our approach on robot reaching tasks, both simulated and in the real world.

Connected through hyperedge HE-1 (induced by source paper "accelerating reinforcement learning for reaching using continuous curriculum learning"):
  with members:
    - "harnessing reinforcement learning for neural motion planning"
    - "interactive open ended object affordance and grasp learning for robotic manipulation"
    - "accuracy based curriculum learning in deep reinforcement learning"
    - "deep learning using rectified linear units relu"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "benchmarking reinforcement learning algorithms on real world robots"
    - "hindsight experience replay"
    - "overcoming exploration in reinforcement learning with demonstrations"

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 "data efficient and safe learning for locomotion via simplified model"):
  with members:
    - "dynamic locomotion for passive ankle biped robots and humanoids using whole body locomotion control"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "end to end safe reinforcement learning through barrier functions for safety critical continuous control tasks"
    - "learning symmetric and low energy locomotion"
    - "model ensemble trust region policy optimization"
    - "stair climbing stabilization of the hrp 4 humanoid robot using whole body admittance control"
    - "fast kinodynamic bipedal locomotion planning with moving obstacles"
    - "a general safety framework for learning based control in uncertain robotic systems"

Connected through hyperedge HE-4 (induced by source paper "ipo interior point policy optimization under constraints"):
  with members:
    - "challenges of real world reinforcement learning"
    - "batch policy learning under constraints"
    - "lyapunov based safe policy optimization for continuous control"
    - "learning dexterous in hand manipulation"
    - "reward constrained policy optimization"
    - "safe exploration in continuous action spaces"
    - "a lyapunov based approach to safe reinforcement learning"
    - "accelerated primal dual policy optimization for safe reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "efficient collaborative multi agent deep reinforcement learning for large scale fleet management"):
  with members:
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "policy gradient with value function approximation for collective multiagent planning"
    - "counterfactual multi agent policy gradients"
    - "magent a many agent reinforcement learning platform for artificial collective intelligence"
    - "deep reinforcement learning a brief survey"
    - "value decomposition networks for cooperative multi agent learning"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "an actor critic algorithm for sequence prediction"

Connected through hyperedge HE-6 (induced by source paper "introspection learning"):
  with members:
    - "ai safety gridworlds"
    - "an approach to reachability analysis for feed forward relu neural networks"
    - "optnet differentiable optimization as a layer in neural networks"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "prioritized experience replay"
    - "continuous control with deep reinforcement learning"
    - "delta decidability over the reals"

Connected through hyperedge HE-7 (induced by source paper "solving online threat screening games using constrained action space reinforcement learning"):
  with members:
    - "resource constrained deep reinforcement learning"
    - "deep reinforcement learning for green security games with real time information"
    - "robust stackelberg equilibria in extensive form games and extension to limited lookahead"
    - "optnet differentiable optimization as a layer in neural networks"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "a survey on policy search algorithms for learning robot controllers in a handful of trials"):
  with members:
    - "learning agile and dynamic motor skills for legged robots"
    - "reset free trial and error learning for robot damage recovery"
    - "an algorithmic perspective on imitation learning"
    - "composable deep reinforcement learning for robotic manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "world models"
    - "sim to real learning agile locomotion for quadruped robots"
    - "state representation learning for control an overview"

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.RO(Robotics)