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 "proximal online gradient is optimum for dynamic regret"):
  Abstract: In online learning, the dynamic regret metric chooses the reference (optimal) solution that may change over time, while the typical (static) regret metric assumes the reference solution to be constant over the whole time horizon. The dynamic regret metric is particularly interesting for applications such as online recommendation (since the customers' preference always evolves over time). While the online gradient method has been shown to be optimal for the static regret metric, the optimal algorithm for the dynamic regret remains unknown. In this paper, we show that proximal online gradient (a general version of online gradient) is optimum to the dynamic regret by showing that the proved lower bound matches the upper bound that slightly improves existing upper bound.

Includes members:
  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 "adaptive online learning in dynamic environments":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "minimizing dynamic regret and adaptive regret simultaneously")
  Member "tracking the best expert in non stationary stochastic environments":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "combinatorial semi bandit in the non stationary environment")
  Member "dynamic regret of strongly adaptive methods":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "minimizing dynamic regret and adaptive regret simultaneously")
  Member "online optimization in dynamic environments improved regret rates for strongly convex problems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "minimizing dynamic regret and adaptive regret simultaneously")
  Member "tracking slowly moving clairvoyant optimal dynamic regret of online learning with true and noisy gradient":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "minimizing dynamic regret and adaptive regret simultaneously")
  Member "online learning to rank with feedback at the top":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "balancing speed and quality in online learning to rank for information retrieval")
  Member "online optimization competing with dynamic comparators":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "minimizing dynamic regret and adaptive regret simultaneously")

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)