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: "convergence of value aggregation for imitation learning"
  Abstract: Value aggregation is a general framework for solving imitation learning problems. Based on the idea of data aggregation, it generates a policy sequence by iteratively interleaving policy optimization and evaluation in an online learning setting. While the existence of a good policy in the policy sequence can be guaranteed non-asymptotically, little is known about the convergence of the sequence or the performance of the last policy. In this paper, we debunk the common belief that value aggregation always produces a convergent policy sequence with improving performance. Moreover, we identify a critical stability condition for convergence and provide a tight non-asymptotic bound on the performance of the last policy. These new theoretical insights let us stabilize problems with regularization, which removes the inconvenient process of identifying the best policy in the policy sequence in stochastic problems.

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"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-2 (induced by source paper "riemannian motion policy fusion through learnable lyapunov function reshaping"):
  with members:
    - "stable concurrent controller composition for multi objective robotic tasks"
    - "representing robot task plans as robust logical dynamical systems"
    - "multi objective policy generation for multi robot systems using riemannian motion policies"
    - "learning latent space dynamics for tactile servoing"
    - "riemannian motion policies"
    - "rmpflow a computational graph for automatic motion policy generation"
    - "fast policy learning through imitation and reinforcement"
    - "approximately optimal continuous time motion planning and control via probabilistic inference"

Connected through hyperedge HE-3 (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"
    - "a dynamic regret analysis and adaptive regularization algorithm for on policy robot 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-4 (induced by source paper "a reduction from reinforcement learning to no regret online learning"):
  with members:
    - "introduction to online convex optimization"
    - "online learning with continuous variations dynamic regret and reductions"
    - "predictor corrector policy optimization"
    - "scalable bilinear pi learning using state and action features"
    - "accelerating imitation learning with predictive models"
    - "boosting the actor with dual critic"
    - "primal dual π learning sample complexity and sublinear run time for ergodic markov decision problems"
    - "a linearly relaxed approximate linear program for markov decision processes"

Connected through hyperedge HE-5 (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"
    - "a dynamic regret analysis and adaptive regularization algorithm for on policy robot 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-6 (induced by source paper "optimal completion distillation for sequence learning"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "token level and sequence level loss smoothing for rnn language models"
    - "switchout an efficient data augmentation algorithm for neural machine translation"
    - "softmax q distribution estimation for structured prediction a theoretical interpretation for raml"
    - "improved training of end to end attention models for speech recognition"
    - "letter based speech recognition with gated convnets"

Connected through hyperedge HE-7 (induced by source paper "predictor corrector policy optimization"):
  with members:
    - "introduction to online convex optimization"
    - "on the convergence of adam and beyond"
    - "universal planning networks"
    - "dual policy iteration"
    - "sim to real learning agile locomotion for quadruped robots"
    - "stochastic variance reduced policy gradient"
    - "accelerating imitation learning with predictive models"
    - "fast policy learning through imitation and reinforcement"

Connected through hyperedge HE-8 (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"
    - "a dynamic regret analysis and adaptive regularization algorithm for on policy robot 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.LG(Machine Learning)