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 "improved regret bounds for projection free bandit convex optimization"):
  Abstract: We revisit the challenge of designing online algorithms for the bandit convex optimization problem (BCO) which are also scalable to high dimensional problems. Hence, we consider algorithms that are \textit{projection-free}, i.e., based on the conditional gradient method whose only access to the feasible decision set, is through a linear optimization oracle (as opposed to other methods which require potentially much more computationally-expensive subprocedures, such as computing Euclidean projections). We present the first such algorithm that attains $O(T^{3/4})$ expected regret using only $O(T)$ overall calls to the linear optimization oracle, in expectation, where $T$ is the number of prediction rounds. This improves over the $O(T^{4/5})$ expected regret bound recently obtained by \cite{Karbasi19}, and actually matches the current best regret bound for projection-free online learning in the \textit{full information} setting.

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 "linear convergence of a frank wolfe type algorithm over trace norm balls":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "faster projection free online learning")
  Member "kernel based methods for bandit convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient and robust algorithms for adversarial linear contextual bandits")
  Member "an optimal algorithm for bandit convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "competing against equilibria in zero sum games with evolving payoffs")
  Member "bandit convex optimization sqrt t regret in one dimension":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "bandit convex optimization in non stationary environments")
  Member "a hybrid algorithm for convex semidefinite optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bilinear factorization for low rank sdp learning")
  Member "block coordinate frank wolfe optimization for structural svms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "safe screening for the generalized conditional gradient method")
  Member "projection free online learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the convergence of stochastic gradient descent with low rank projections for convex low rank matrix problems")

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)