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 "trading off static and dynamic regret in online least squares and beyond"):
  Abstract: Recursive least-squares algorithms often use forgetting factors as a heuristic to adapt to non-stationary data streams. % The first contribution of this paper rigorously characterizes the effect of forgetting factors for a class of online Newton algorithms. % For exp-concave and strongly convex objectives, the algorithms achieve a dynamic regret of $\max\{O(\log T),O(\sqrt{TV})\}$, where $V$ is a bound on the path length of the comparison sequence. #R##N#% In particular, we show how classic recursive least-squares with a forgetting factor achieves this dynamic regret bound. % By varying $V$, we obtain a trade-off between static and dynamic regret. % Furthermore, we show how the forgetting factor can be tuned to obtain % trade-offs between static and dynamic regret. % In order to obtain more computationally efficient algorithms, our second contribution is a novel gradient descent step size rule for strongly convex functions. % Our gradient descent rule recovers the dynamic regret bounds described above. % For smooth problems, we can also obtain static regret of $O(T^{1-\beta})$ and dynamic regret of $O(T^\beta V^*)$, where $\beta \in (0,1)$ and $V^*$ is the path length of the sequence of minimizers. % By varying $\beta$, we obtain a trade-off between static and dynamic regret.

Includes members:
  Member "weighted linear bandits for non stationary environments":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficient and robust algorithms for adversarial linear contextual bandits")
  Member "online adaptive principal component analysis and its extensions":
    also connected through other hyperedges:
      (none)
  Member "introduction to online convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")
  Member "online convex optimization for cumulative constraints":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "simple and fast algorithm for binary integer and online linear programming")
  Member "adaptive online learning in dynamic environments":
    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")

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)