human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "continuous online learning and new insights to online imitation learning"):
  Abstract: Online learning is a powerful tool for analyzing iterative algorithms. However, the classic adversarial setup sometimes fails to capture certain regularity in online problems in practice. Motivated by this, we establish a new setup, called Continuous Online Learning (COL), where the gradient of online loss function changes continuously across rounds with respect to the learner's decisions. We show that COL covers and more appropriately describes many interesting applications, from general equilibrium problems (EPs) to optimization in episodic MDPs. Using this new setup, we revisit the difficulty of achieving sublinear dynamic regret. We prove that there is a fundamental equivalence between achieving sublinear dynamic regret in COL and solving certain EPs, and we present a reduction from dynamic regret to both static regret and convergence rate of the associated EP. At the end, we specialize these new insights into online imitation learning and show improved understanding of its learning stability.

Includes members:
  Member "a reduction from reinforcement learning to no regret online learning":
    also connected through other hyperedges:
      (none)
  Member "introduction to online convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")
  Member "online learning with continuous variations dynamic regret and reductions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on policy robot imitation learning from a converging supervisor")
  Member "predictor corrector policy optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "imitation projected programmatic reinforcement learning")
  Member "convergence of value aggregation for imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on policy robot imitation learning from a converging supervisor")
  Member "a dynamic regret analysis and adaptive regularization algorithm for on policy robot imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on policy robot imitation learning from a converging supervisor")
  Member "accelerating imitation learning with predictive models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "imitation projected programmatic reinforcement learning")
  Member "deeply aggrevated differentiable imitation learning for sequential prediction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "exploration efficient deep reinforcement learning with demonstration guidance for robot control")

Question: Please tell me which class the source paper 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)