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: "learning to walk via deep reinforcement learning"
  Abstract: Deep reinforcement learning (deep RL) holds the promise of automating the acquisition of complex controllers that can map sensory inputs directly to low-level actions. In the domain of robotic locomotion, deep RL could enable learning locomotion skills with minimal engineering and without an explicit model of the robot dynamics. Unfortunately, applying deep RL to real-world robotic tasks is exceptionally difficult, primarily due to poor sample complexity and sensitivity to hyperparameters. While hyperparameters can be easily tuned in simulated domains, tuning may be prohibitively expensive on physical systems, such as legged robots, that can be damaged through extensive trial-and-error learning. In this paper, we propose a sample-efficient deep RL algorithm based on maximum entropy RL that requires minimal per-task tuning and only a modest number of trials to learn neural network policies. We apply this method to learning walking gaits on a real-world Minitaur robot. Our method can acquire a stable gait from scratch directly in the real world in about two hours, without relying on any model or simulation, and the resulting policy is robust to moderate variations in the environment. We further show that our algorithm achieves state-of-the-art performance on simulated benchmarks with a single set of hyperparameters. Videos of training and the learned policy can be found on the project website.

Connected through hyperedge HE-1 (induced by source paper "learning to fly via deep model based reinforcement learning"):
  with members:
    - "deep drone racing from simulation to reality with domain randomization"
    - "solving rubik s cube with a robot hand"
    - "imagined value gradients model based policy optimization with transferable latent dynamics models"
    - "flying through a narrow gap using neural network an end to end planning and control approach"
    - "teach repeat replan a complete and robust system for aggressive flight in complex environments"
    - "neuroflight next generation flight control firmware"
    - "robust and efficient quadrotor trajectory generation for fast autonomous flight"
    - "real time reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "rapidly adaptable legged robots via evolutionary meta learning"):
  with members:
    - "es maml simple hessian free meta learning"
    - "active domain randomization"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "varibad a very good method for bayes adaptive deep rl via meta learning"
    - "multi agent manipulation via locomotion using hierarchical sim2real"
    - "norml no reward meta learning"
    - "learning generalizable locomotion skills with hierarchical reinforcement learning"
    - "learning agile and dynamic motor skills for legged robots"

Connected through hyperedge HE-3 (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-4 (induced by source paper "particle based adaptive discretization for continuous control using deep reinforcement learning"):
  with members:
    - "discretizing continuous action space for on policy optimization"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "sim to real learning agile locomotion for quadruped robots"
    - "sfv reinforcement learning of physical skills from videos"
    - "soft actor critic algorithms and applications"
    - "discrete sequential prediction of continuous actions for deep rl"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-5 (induced by source paper "periodic intra ensemble knowledge distillation for reinforcement learning"):
  with members:
    - "population guided parallel policy search for reinforcement learning"
    - "information asymmetry in kl regularized rl"
    - "knowledge distillation by on the fly native ensemble"
    - "off policy deep reinforcement learning without exploration"
    - "evolution guided policy gradient in reinforcement learning"
    - "soft actor critic algorithms and applications"
    - "deep mutual learning"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"

Connected through hyperedge HE-6 (induced by source paper "on simple reactive neural networks for behaviour based reinforcement learning"):
  with members:
    - "vid2param online system identification from video for robotics applications"
    - "composable deep reinforcement learning for robotic manipulation"
    - "silhonet an rgb method for 6d object pose estimation"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "investigating human priors for playing video games"
    - "assessing generalization in deep reinforcement learning"
    - "a dissection of overfitting and generalization in continuous reinforcement learning"
    - "learning multi level hierarchies with hindsight"

Connected through hyperedge HE-7 (induced by source paper "real world human robot collaborative reinforcement learning"):
  with members:
    - "dota 2 with large scale deep reinforcement learning"
    - "human visual attention prediction boosts learning performance of autonomous driving agents"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "pytorch an imperative style high performance deep learning library"
    - "soft actor critic algorithms and applications"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "shared autonomy via deep reinforcement learning"
    - "machine theory of mind"

Connected through hyperedge HE-8 (induced by source paper "control frequency adaptation via action persistence in batch reinforcement learning"):
  with members:
    - "reinforcement learning for robotic manipulation using simulated locomotion demonstrations"
    - "making deep q learning methods robust to time discretization"
    - "proximal policy optimization algorithms"
    - "the option critic architecture"
    - "continuous control with deep reinforcement learning"
    - "trust region policy optimization"

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)