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: "online stochastic matching online actions based on offline statistics"
  Abstract: We consider the online stochastic matching problem proposed by Feldman et al. [FMMM09] as a model of display ad allocation. We are given a bipartite graph; one side of the graph corresponds to a fixed set of bins and the other side represents the set of possible ball types. At each time step, a ball is sampled independently from the given distribution and it needs to be matched upon its arrival to an empty bin. The goal is to maximize the number of allocations. #R##N#We present an online algorithm for this problem with a competitive ratio of 0.702. Before our result, algorithms with a competitive ratio better than $1-1/e$ were known under the assumption that the expected number of arriving balls of each type is integral. A key idea of the algorithm is to collect statistics about the decisions of the optimum offline solution using Monte Carlo sampling and use those statistics to guide the decisions of the online algorithm. We also show that our algorithm achieves a competitive ratio of 0.705 when the rates are integral. #R##N#On the hardness side, we prove that no online algorithm can have a competitive ratio better than 0.823 under the known distribution model (and henceforth under the permutation model). This improves upon the 5/6 hardness result proved by Goel and Mehta \cite{GM08} for the permutation model.

Connected through hyperedge HE-1 (induced by source paper "online graph matching problems with a worst case reassignment budget"):
  with members:
    - "beating greedy for stochastic bipartite matching"
    - "online matching with general arrivals"
    - "stochastic online metric matching"
    - "understanding zadimoghaddam s edge weighted online matching algorithm unweighted case"
    - "tight competitive ratios of classic matching algorithms in the fully online model"
    - "online maximum matching with recourse"
    - "how to match when all vertices arrive online"
    - "online vertex weighted bipartite matching beating 1 1 e with random arrivals"

Connected through hyperedge HE-2 (induced by source paper "online primal dual meets online matching with stochastic rewards configuration lp to the rescue"):
  with members:
    - "vertex weighted online stochastic matching with patience constraints"
    - "online matching with stochastic rewards optimal competitive ratio via path based formulation"
    - "tight competitive ratios of classic matching algorithms in the fully online model"
    - "how to match when all vertices arrive online"
    - "maximum weight online matching with deadlines"
    - "online vertex weighted bipartite matching beating 1 1 e with random arrivals"
    - "online vertex weighted bipartite matching and single bid budgeted allocations"
    - "online stochastic matching beating 1 1 e"

Connected through hyperedge HE-3 (induced by source paper "prophet inequality for bipartite matching merits of being simple and non adaptive"):
  with members:
    - "near optimal online algorithms and fast approximation algorithms for resource allocation problems"
    - "tight competitive ratios of classic matching algorithms in the fully online model"
    - "how to match when all vertices arrive online"
    - "maximizing efficiency in dynamic matching markets"
    - "makespan minimization via posted prices"
    - "prophet inequalities made easy stochastic optimization by pricing non stochastic inputs"
    - "beyond matroids secretary problem and prophet inequality with general constraints"
    - "online contention resolution schemes"

Connected through hyperedge HE-4 (induced by source paper "beating greedy for stochastic bipartite matching"):
  with members:
    - "the price of information in combinatorial optimization"
    - "the stochastic matching problem beating half with a non adaptive algorithm"
    - "prophet secretary for combinatorial auctions and matroids"
    - "almost optimal stochastic weighted matching with few queries"
    - "stochastic packing integer programs with few queries"
    - "new algorithms better bounds and a novel model for online stochastic matching"
    - "ignorance is almost bliss near optimal stochastic matching with few queries"
    - "improved approximation algorithms for stochastic matching"

Connected through hyperedge HE-5 (induced by source paper "the bayesian prophet a low regret framework for online decision making"):
  with members:
    - "near optimal online algorithms and fast approximation algorithms for resource allocation problems"
    - "online stochastic matching new algorithms and bounds"
    - "prophet inequalities made easy stochastic optimization by pricing non stochastic inputs"
    - "new algorithms better bounds and a novel model for online stochastic matching"
    - "online convex optimization using predictions"
    - "algorithms with logarithmic or sublinear regret for constrained contextual bandits"
    - "fast algorithms for online stochastic convex programming"
    - "bandits with knapsacks"

Connected through hyperedge HE-6 (induced by source paper "online stochastic matching with edge arrivals"):
  with members:
    - "beating greedy for stochastic bipartite matching"
    - "online matching with general arrivals"
    - "prophet inequality for bipartite matching merits of being simple and non adaptive"
    - "tight competitive ratios of classic matching algorithms in the fully online model"
    - "how to match when all vertices arrive online"
    - "online matroid intersection beating half for random arrival"
    - "improved approximation algorithms for stochastic matching"
    - "improved bounds for online preemptive matching"

Connected through hyperedge HE-7 (induced by source paper "vertex weighted online stochastic matching with patience constraints"):
  with members:
    - "online matching with stochastic rewards optimal competitive ratio via path based formulation"
    - "greedy bipartite matching in random type poisson arrival model"
    - "attenuate locally win globally an attenuation based framework for online stochastic matching with timeouts"
    - "an experimental study of algorithms for online bipartite matching"
    - "online vertex weighted bipartite matching beating 1 1 e with random arrivals"
    - "new algorithms better bounds and a novel model for online stochastic matching"
    - "improved approximation algorithms for stochastic matching"
    - "greedy online bipartite matching on random graphs"

Connected through hyperedge HE-8 (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 packing applied to display ad allocation"
    - "a dynamic near optimal algorithm for online linear programming"
    - "online stochastic matching beating 1 1 e"
    - "randomized rounding without solving the linear program"

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)