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 "dynamic online gradient descent with improved query complexity a theoretical revisit"):
  Abstract: We provide a new theoretical analysis framework to investigate online gradient descent in the dynamic environment. Comparing with the previous work, the new framework recovers the state-of-the-art dynamic regret, but does not require extra gradient queries for every iteration. Specifically, when functions are $\alpha$ strongly convex and $\beta$ smooth, to achieve the state-of-the-art dynamic regret, the previous work requires $O(\kappa)$ with $\kappa = \frac{\beta}{\alpha}$ queries of gradients at every iteration. But, our framework shows that the query complexity can be improved to be $O(1)$, which does not depend on $\kappa$. The improvement is significant for ill-conditioned problems because that their objective function usually has a large $\kappa$.

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 "fast rates for online gradient descent without strong convexity via hoffman s bound":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "provable guarantees for gradient based meta learning")
  Member "convergence of unregularized online learning algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stochastic gradient descent for nonconvex learning without bounded gradient assumptions")
  Member "a modular analysis of adaptive non convex optimization optimism composite objectives and variational bounds":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "provably efficient adaptive approximate policy iteration")
  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 "streaming recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "autoemb automated embedding dimensionality search in streaming recommendations")
  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)