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 near optimal dynamic learning algorithm for online matching problems with concave returns"
  Abstract: We consider an online matching problem with concave returns. This problem is a significant generalization of the Adwords allocation problem and has vast applications in online advertising. In this problem, a sequence of items arrive sequentially and each has to be allocated to one of the bidders, who bid a certain value for each item. At each time, the decision maker has to allocate the current item to one of the bidders without knowing the future bids and the objective is to maximize the sum of some concave functions of each bidder's aggregate value. In this work, we propose an algorithm that achieves near-optimal performance for this problem when the bids arrive in a random order and the input data satisfies certain conditions. The key idea of our algorithm is to learn the input data pattern dynamically: we solve a sequence of carefully chosen partial allocation problems and use their optimal solutions to assist with the future decision. Our analysis belongs to the primal-dual paradigm, however, the absence of linearity of the objective function and the dynamic feature of the algorithm makes our analysis quite unique.

Connected through hyperedge HE-1 (induced by source paper "bernoulli factories and black box reductions in mechanism design"):
  with members:
    - "near optimal online algorithms and fast approximation algorithms for resource allocation problems"
    - "non optimal mechanism design"
    - "fast algorithms for online stochastic convex programming"
    - "bandits with knapsacks"
    - "multi parameter mechanisms with implicit payment computation"
    - "primal beats dual on online packing lps in the random order model"
    - "the exponential mechanism for social welfare private truthful and nearly optimal"
    - "limitations of randomized mechanisms for combinatorial auctions"

Connected through hyperedge HE-2 (induced by source paper "linear contextual bandits with global constraints and objective"):
  with members:
    - "algorithms with logarithmic or sublinear regret for constrained contextual bandits"
    - "bandits with concave rewards and convex knapsacks"
    - "fast algorithms for online stochastic convex programming"
    - "resourceful contextual bandits"
    - "how the experts algorithm can help solve lps online"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "online decision making in crowdsourcing markets theoretical challenges position paper"
    - "bandits with knapsacks"

Connected through hyperedge HE-3 (induced by source paper "fast algorithms for online stochastic convex programming"):
  with members:
    - "near optimal online algorithms and fast approximation algorithms for resource allocation problems"
    - "bandits with concave rewards and convex knapsacks"
    - "how the experts algorithm can help solve lps online"
    - "bandits with knapsacks"
    - "ad serving using a compact allocation plan"
    - "dynamic pricing with limited supply"
    - "online vertex weighted bipartite matching and single bid budgeted allocations"
    - "online stochastic matching online actions based on offline statistics"

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)