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 "revisiting projection free optimization for strongly convex constraint sets"):
  Abstract: We revisit the Frank-Wolfe (FW) optimization under strongly convex constraint sets. We provide a faster convergence rate for FW without line search, showing that a previously overlooked variant of FW is indeed faster than the standard variant. With line search, we show that FW can converge to the global optimum, even for smooth functions that are not convex, but are quasi-convex and locally-Lipschitz. We also show that, for the general case of (smooth) non-convex functions, FW with line search converges with high probability to a stationary point at a rate of $O\left(\frac{1}{t}\right)$, as long as the constraint set is strongly convex -- one of the fastest convergence rates in non-convex optimization.

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 "following the leader and fast rates in linear prediction curved constraint sets and other regularities":
    also connected through other hyperedges:
      (none)
  Member "minding the gaps for block frank wolfe optimization of structured svms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "curvature of feasible sets in offline and online optimization")
  Member "escaping from saddle points online stochastic gradient for tensor decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "robust optimization for deep regression":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an efficient method of training small models for regression problems with knowledge distillation")
  Member "on pairwise costs for network flow multi object tracking":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unsupervised multiple person tracking using autoencoder based lifted multicuts")
  Member "projection free online learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the convergence of stochastic gradient descent with low rank projections for convex low rank matrix problems")
  Member "large scale convex minimization with a low rank constraint":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "introduction to online convex optimization")

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)