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: "plato policy learning using adaptive trajectory optimization"
  Abstract: Policy search can in principle acquire complex strategies for control of robots and other autonomous systems. When the policy is trained to process raw sensory inputs, such as images and depth maps, it can also acquire a strategy that combines perception and control. However, effectively processing such complex inputs requires an expressive policy class, such as a large neural network. These high-dimensional policies are difficult to train, especially when learning to control safety-critical systems. We propose PLATO, an algorithm that trains complex control policies with supervised learning, using model-predictive control (MPC) to generate the supervision, hence never in need of running a partially trained and potentially unsafe policy. PLATO uses an adaptive training method to modify the behavior of MPC to gradually match the learned policy in order to generate training samples at states that are likely to be visited by the learned policy. PLATO also maintains the MPC cost as an objective to avoid highly undesirable actions that would result from strictly following the learned policy before it has been fully trained. We prove that this type of adaptive MPC expert produces supervision that leads to good long-horizon performance of the resulting policy. We also empirically demonstrate that MPC can still avoid dangerous on-policy actions in unexpected situations during training. Our empirical results on a set of challenging simulated aerial vehicle tasks demonstrate that, compared to prior methods, PLATO learns faster, experiences substantially fewer catastrophic failures (crashes) during training, and often converges to a better policy.

Connected through hyperedge HE-1 (induced by source paper "adapt zero shot adaptive policy transfer for stochastic dynamical systems"):
  with members:
    - "sim to real transfer of robotic control with dynamics randomization"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "progressive neural networks"
    - "robust adversarial reinforcement learning"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "sequential linear quadratic optimal control for nonlinear switched systems"
    - "epopt learning robust neural network policies using model ensembles"
    - "continuous deep q learning with model based acceleration"

Connected through hyperedge HE-2 (induced by source paper "mpc guided imitation learning of neural network policies for the artificial pancreas"):
  with members:
    - "pac confidence sets for deep neural networks via calibrated prediction"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "plan online learn offline efficient learning and exploration via model based control"
    - "safe end to end imitation learning for model predictive control"
    - "representation and reinforcement learning for personalized glycemic control in septic patients"
    - "dropoutdagger a bayesian approach to safe imitation learning"
    - "data driven robust control for type 1 diabetes under meal and exercise uncertainties"
    - "generative adversarial imitation learning"

Connected through hyperedge HE-3 (induced by source paper "two body problem collaborative visual task completion"):
  with members:
    - "tarmac targeted multi agent communication"
    - "understanding grounded language learning agents"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "neural modular control for embodied question answering"
    - "minos multimodal indoor simulator for navigation in complex environments"
    - "unifying map and landmark based representations for visual navigation"
    - "home a household multimodal environment"
    - "learning cooperative visual dialog agents with deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "guided meta policy search"):
  with members:
    - "some considerations on learning to explore via meta reinforcement learning"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "meta reinforcement learning of structured exploration strategies"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "evolved policy gradients"
    - "promp proximal meta policy search"
    - "learning to adapt meta learning for model based control"
    - "one shot visual imitation learning via meta learning"

Connected through hyperedge HE-5 (induced by source paper "p3o policy on policy off policy optimization"):
  with members:
    - "the mirage of action dependent baselines in reinforcement learning"
    - "self imitation learning"
    - "addressing function approximation error in actor critic methods"
    - "are deep policy gradient algorithms truly policy gradient algorithms"
    - "deep reinforcement learning that matters"
    - "a distributional perspective on reinforcement learning"
    - "interpolated policy gradient merging on policy and off policy gradient estimation for deep reinforcement learning"
    - "bridging the gap between value and policy based reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "on policy robot imitation learning from a converging supervisor"):
  with members:
    - "extending deep model predictive control with safety augmented value estimation from demonstrations"
    - "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"
    - "introduction to online convex optimization"
    - "online learning with continuous variations dynamic regret and reductions"
    - "dual policy iteration"
    - "model ensemble trust region policy optimization"
    - "addressing function approximation error in actor critic methods"
    - "convergence of value aggregation for imitation learning"

Connected through hyperedge HE-7 (induced by source paper "bayesian residual policy optimization scalable bayesian reinforcement learning with clairvoyant experts"):
  with members:
    - "meta learning of sequential strategies"
    - "lets drive driving in a crowd by learning from tree search"
    - "meta learners learning dynamics are unlike learners"
    - "some considerations on learning to explore via meta reinforcement learning"
    - "exploration by random network distillation"
    - "residual policy learning"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "sim to real transfer of robotic control with dynamics randomization"

Connected through hyperedge HE-8 (induced by source paper "mpc net a first principles guided policy search"):
  with members:
    - "feedback mpc for torque controlled legged robots"
    - "data efficient reinforcement learning for legged robots"
    - "iterative reinforcement learning based design of dynamic locomotion skills for cassie"
    - "on the convergence of adam and beyond"
    - "learning agile and dynamic motor skills for legged robots"
    - "policies modulating trajectory generators"
    - "an algorithmic perspective on imitation learning"
    - "sim to real learning agile locomotion for quadruped robots"

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)