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 beating 1 1 e"
  Abstract: We study the online stochastic bipartite matching problem, in a form motivated by display ad allocation on the Internet. In the online, but adversarial case, the celebrated result of Karp, Vazirani and Vazirani gives an approximation ratio of $1-1/e$. In the online, stochastic case when nodes are drawn repeatedly from a known distribution, the greedy algorithm matches this approximation ratio, but still, no algorithm is known that beats the $1 - 1/e$ bound. #R##N#Our main result is a 0.67-approximation online algorithm for stochastic bipartite matching, breaking this $1 - {1/e}$ barrier. Furthermore, we show that no online algorithm can produce a $1-\epsilon$ approximation for an arbitrarily small $\epsilon$ for this problem. #R##N#We employ a novel application of the idea of the power of two choices from load balancing: we compute two disjoint solutions to the expected instance, and use both of them in the online algorithm in a prescribed preference order. #R##N#To identify these two disjoint solutions, we solve a max flow problem in a boosted flow graph, and then carefully decompose this maximum flow to two edge-disjoint (near-)matchings. These two offline solutions are used to characterize an upper bound for the optimum in any scenario. This is done by identifying a cut whose value we can bound under the arrival distribution.

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 online actions based on offline statistics"

Connected through hyperedge HE-3 (induced by source paper "online policies for efficient volunteer crowdsourcing"):
  with members:
    - "allocation problems in ride sharing platforms online matching with offline reusable resources"
    - "guaranteed non convex optimization submodular maximization over continuous domains"
    - "bayesian combinatorial auctions expanding single buyer mechanisms to many buyers"
    - "budget optimal task allocation for reliable crowdsourcing systems"

Connected through hyperedge HE-4 (induced by source paper "online allocation of reusable resources achieving optimal competitive ratio"):
  with members:
    - "online matching with stochastic rewards optimal competitive ratio via path based formulation"
    - "algorithms for online matching assortment and pricing with tight weight dependent competitive ratios"
    - "allocation problems in ride sharing platforms online matching with offline reusable resources"
    - "budget optimal task allocation for reliable crowdsourcing systems"

Connected through hyperedge HE-5 (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-6 (induced by source paper "tight bounds for online edge coloring"):
  with members:
    - "online matching with general arrivals"
    - "tight competitive ratios of classic matching algorithms in the fully online model"
    - "how to match when all vertices arrive online"
    - "an economic based analysis of ranking for online bipartite matching"
    - "tighter bounds for online bipartite matching"
    - "deterministic distributed edge coloring with fewer colors"
    - "dynamic algorithms for graph coloring"
    - "deterministic distributed edge coloring via hypergraph maximal matching"

Connected through hyperedge HE-7 (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-8 (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"

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)