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: "a dynamic near optimal algorithm for online linear programming"
  Abstract: A natural optimization model that formulates many online resource allocation and revenue management problems is the online linear program (LP) in which the constraint matrix is revealed column by column along with the corresponding objective coefficient. In such a model, a decision variable has to be set each time a column is revealed without observing the future inputs and the goal is to maximize the overall objective function. In this paper, we provide a near-optimal algorithm for this general class of online problems under the assumption of random order of arrival and some mild conditions on the size of the LP right-hand-side input. Specifically, our learning-based algorithm works by dynamically updating a threshold price vector at geometric time intervals, where the dual prices learned from the revealed columns in the previous period are used to determine the sequential decisions in the current period. Due to the feature of dynamic learning, the competitiveness of our algorithm improves over the past study of the same problem. We also present a worst-case example showing that the performance of our algorithm is near-optimal.

Connected through hyperedge HE-1 (induced by source paper "random order models"):
  with members:
    - "robust algorithms for the secretary problem"
    - "prophet secretary for combinatorial auctions and matroids"
    - "combinatorial prophet inequalities"
    - "beyond matroids secretary problem and prophet inequality with general constraints"
    - "secretary problems with non uniform arrival order"
    - "o log log rank competitive ratio for the matroid secretary problem"
    - "prophet inequalities with limited information"
    - "primal beats dual on online packing lps in the random order model"

Connected through hyperedge HE-2 (induced by source paper "simple and fast algorithm for binary integer and online linear programming"):
  with members:
    - "online linear programming dual convergence new algorithms and regret bounds"
    - "how good is sgd with random shuffling"
    - "introduction to online convex optimization"
    - "online convex optimization for cumulative constraints"
    - "a linearly relaxed approximate linear program for markov decision processes"
    - "fast algorithms for online stochastic convex programming"
    - "a simpler approach to obtaining an o 1 t convergence rate for the projected stochastic subgradient method"
    - "trading regret for efficiency online convex optimization with long term constraints"

Connected through hyperedge HE-3 (induced by source paper "competing against equilibria in zero sum games with evolving payoffs"):
  with members:
    - "introduction to online convex optimization"
    - "on the convergence of adam and beyond"
    - "the mechanics of n player differentiable games"
    - "on convergence and stability of gans"
    - "faster rates for convex concave games"
    - "adversarial bandits with knapsacks"
    - "began boundary equilibrium generative adversarial networks"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-4 (induced by source paper "near optimal online algorithms and fast approximation algorithms for resource allocation problems"):
  with members:
    - "fast algorithms for online stochastic convex programming"
    - "primal beats dual on online packing lps in the random order model"
    - "online submodular welfare maximization greedy is optimal"
    - "selective call out and real time bidding"
    - "online stochastic matching online actions based on offline statistics"
    - "online stochastic packing applied to display ad allocation"
    - "online stochastic matching beating 1 1 e"
    - "randomized rounding without solving the linear program"

Connected through hyperedge HE-5 (induced by source paper "bid optimization by multivariable control in display advertising"):
  with members:
    - "budget constrained bidding by model free reinforcement learning in display advertising"
    - "bidding machine learning to bid for directly optimizing profits in display advertising"
    - "real time bidding by reinforcement learning in display advertising"
    - "feedback control of real time display advertising"
    - "smart pacing for effective online ad campaign optimization"
    - "real time bidding for online advertising measurement and analysis"
    - "real time bid optimization with smooth budget delivery in online advertising"

Connected through hyperedge HE-6 (induced by source paper "online linear programming dual convergence new algorithms and regret bounds"):
  with members:
    - "near optimal online algorithms and fast approximation algorithms for resource allocation problems"
    - "introduction to online convex optimization"
    - "a primal dual learning algorithm for personalized dynamic pricing with an inventory constraint"
    - "algorithms with logarithmic or sublinear regret for constrained contextual bandits"
    - "fast algorithms for online stochastic convex programming"

Connected through hyperedge HE-7 (induced by source paper "online multidimensional packing problems in the random order model"):
  with members:
    - "primal beats dual on online packing lps in the random order model"
    - "geometry of online packing linear programs"
    - "online vertex weighted bipartite matching and single bid budgeted allocations"
    - "online stochastic packing applied to display ad allocation"
    - "combinatorial auctions with decreasing marginal utilities"

Connected through hyperedge HE-8 (induced by source paper "adversarial bandits with knapsacks"):
  with members:
    - "near optimal online algorithms and fast approximation algorithms for resource allocation problems"
    - "introduction to online convex optimization"
    - "introduction to multi armed bandits"
    - "bandit convex optimization for scalable and dynamic iot management"
    - "acceleration through optimistic no regret dynamics"
    - "stochastic bandits robust to adversarial corruptions"
    - "more adaptive algorithms for adversarial bandits"
    - "unifying the stochastic and the adversarial bandits with knapsack"

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)