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: "a dynamic regret analysis and adaptive regularization algorithm for on policy robot imitation learning"
  Abstract: On-policy imitation learning algorithms such as Dagger evolve a robot control policy by executing it, measuring performance (loss), obtaining corrective feedback from a supervisor, and generating the next policy. As the loss between iterations can vary unpredictably, a fundamental question is under what conditions this process will eventually achieve a converged policy. If one assumes the underlying trajectory distribution is static (stationary), it is possible to prove convergence for Dagger. Cheng and Boots (2018) consider the more realistic model for robotics where the underlying trajectory distribution, which is a function of the policy, is dynamic and show that it is possible to prove convergence when a condition on the rate of change of the trajectory distributions is satisfied. In this paper, we reframe that result using dynamic regret theory from the field of Online Optimization to prove convergence to locally optimal policies for Dagger, Imitation Gradient, and Multiple Imitation Gradient. These results inspire a new algorithm, Adaptive On-Policy Regularization (AOR), that ensures the conditions for convergence. We present simulation results with cart-pole balancing and walker locomotion benchmarks that suggest AOR can significantly decrease dynamic regret and chattering. To our knowledge, this the first application of dynamic regret theory to imitation learning.

Connected through hyperedge HE-1 (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-2 (induced by source paper "continuous online learning and new insights to online imitation learning"):
  with members:
    - "a reduction from reinforcement learning to no regret online learning"
    - "introduction to online convex optimization"
    - "online learning with continuous variations dynamic regret and reductions"
    - "predictor corrector policy optimization"
    - "convergence of value aggregation for imitation learning"
    - "accelerating imitation learning with predictive models"
    - "deeply aggrevated differentiable imitation learning for sequential prediction"
    - "online optimization in dynamic environments improved regret rates for strongly convex problems"

Connected through hyperedge HE-3 (induced by source paper "online learning with continuous variations dynamic regret and reductions"):
  with members:
    - "introduction to online convex optimization"
    - "dynamic regret convergence analysis and an adaptive regularization algorithm for on policy robot imitation learning"
    - "convergence of value aggregation for imitation learning"
    - "accelerating imitation learning with predictive models"
    - "online optimization in dynamic environments improved regret rates for strongly convex problems"
    - "tracking slowly moving clairvoyant optimal dynamic regret of online learning with true and noisy gradient"
    - "online optimization competing with dynamic comparators"
    - "agnostic system identification for model based reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "dynamic regret convergence analysis and an adaptive regularization algorithm for on policy robot imitation learning"):
  with members:
    - "introduction to online convex optimization"
    - "online learning with continuous variations dynamic regret and reductions"
    - "imitation learning as f divergence minimization"
    - "an algorithmic perspective on imitation learning"
    - "lipschitz continuity in model based reinforcement learning"
    - "convergence of value aggregation for imitation learning"
    - "accelerating imitation learning with predictive models"
    - "fast policy learning through imitation and reinforcement"

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)