human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "optimal online two way trading with bounded number of transactions"
  Abstract: We consider a two-way trading problem, where investors buy and sell a stock whose price moves within a certain range. Naturally they want to maximize their profit. Investors can perform up to $k$ trades, where each trade must involve the full amount. We give optimal algorithms for three different models which differ in the knowledge of how the price fluctuates. In the first model, there are global minimum and maximum bounds $m$ and $M$. We first show an optimal lower bound of $\varphi$ (where $\varphi=M/m$) on the competitive ratio for one trade, which is the bound achieved by trivial algorithms. Perhaps surprisingly, when we consider more than one trade, we can give a better algorithm that loses only a factor of $\varphi^{2/3}$ (rather than $\varphi$) per additional trade. Specifically, for $k$ trades the algorithm has competitive ratio $\varphi^{(2k+1)/3}$. Furthermore we show that this ratio is the best possible by giving a matching lower bound. In the second model, $m$ and $M$ are not known in advance, and just $\varphi$ is known. We show that this only costs us an extra factor of $\varphi^{1/3}$, i.e., both upper and lower bounds become $\varphi^{(2k+2)/3}$. Finally, we consider the bounded daily return model where instead of a global limit, the fluctuation from one day to the next is bounded, and again we give optimal algorithms, and interestingly one of them resembles common trading strategies that involve stop loss limits.

Connected through hyperedge HE-1 (induced by source paper "ngo gm natural gradient optimization for graphical models"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "the cma evolution strategy a tutorial"
    - "revisiting natural gradient for deep networks"
    - "analysis of a natural gradient algorithm on monotonic convex quadratic composite functions"
    - "convergence of the continuous time trajectories of isotropic evolution strategies on monotonic c 2 composite functions"

Connected through hyperedge HE-2 (induced by source paper "a new approach to learning in dynamic bayesian networks dbns"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "expectation maximization for adaptive mixture models in graph optimization"
    - "convergence of the continuous time trajectories of isotropic evolution strategies on monotonic c 2 composite functions"

Question: Please tell me which class the center node 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.DS(Data Structures and Algorithms)