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 "online newton step algorithm with estimated gradient"):
  Abstract: Online learning with limited information feedback (bandit) tries to solve the problem where an online learner receives partial feedback information from the environment in the course of learning. Under this setting, Flaxman et al.[8] extended Zinkevich's classical Online Gradient Descent (OGD) algorithm [29] by proposing the Online Gradient Descent with Expected Gradient (OGDEG) algorithm. Specifically, it uses a simple trick to approximate the gradient of the loss function $f_t$ by evaluating it at a single point and bounds the expected regret as $\mathcal{O}(T^{5/6})$ [8], where the number of rounds is $T$. Meanwhile, past research efforts have shown that compared with the first-order algorithms, second-order online learning algorithms such as Online Newton Step (ONS) [11] can significantly accelerate the convergence rate of traditional online learning algorithms. Motivated by this, this paper aims to exploit the second-order information to speed up the convergence of the OGDEG algorithm. In particular, we extend the ONS algorithm with the trick of expected gradient and develop a novel second-order online learning algorithm, i.e., Online Newton Step with Expected Gradient (ONSEG). Theoretically, we show that the proposed ONSEG algorithm significantly reduces the expected regret of OGDEG algorithm from $\mathcal{O}(T^{5/6})$ to $\mathcal{O}(T^{2/3})$ in the bandit feedback scenario. Empirically, we further demonstrate the advantages of the proposed algorithm on multiple real-world datasets.

Includes members:
  Member "online learning a comprehensive survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bio inspired digit recognition using reward modulated spike timing dependent plasticity in deep convolutional networks")
  Member "stochastic optimization with bandit sampling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi resolution hashing for fast pairwise summations")
  Member "online learning to rank in stochastic click models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "be aware of non stationarity nearly optimal algorithms for piecewise stationary cascading bandits")
  Member "kernel based methods for bandit convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "competing against equilibria in zero sum games with evolving payoffs")
  Member "on graduated optimization for stochastic non convex problems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed stochastic algorithms for high rate streaming principal component analysis")
  Member "on the complexity of bandit and derivative free stochastic convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "naive exploration is optimal for online lqr")
  Member "online convex optimization in the bandit setting gradient descent without a gradient":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "accelerating reinforcement learning with a directional gaussian smoothing evolution strategy")

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)