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: "beating greedy for stochastic bipartite matching"
  Abstract: We consider the maximum bipartite matching problem in stochastic settings, namely the query-commit and price-of-information models. In the query-commit model, an edge e independently exists with probability pe. We can query whether an edge exists or not, but if it does exist, then we have to take it into our solution. In the unweighted case, one can query edges in the order given by the classical online algorithm of Karp, Vazirani, and Vazirani [20] to get a (1 − 1/e)-approximation. In contrast, the previously best known algorithm in the weighted case is the (1/2)-approximation achieved by the greedy algorithm that sorts the edges according to their weights and queries in that order.#R##N##R##N#Improving upon the basic greedy, we give a (1 -1/e)-approximation algorithm in the weighted query-commit model. We use a linear program (LP) to upper bound the optimum achieved by any strategy. The proposed LP admits several structural properties that play a crucial role in the design and analysis of our algorithm. We also extend these techniques to get a (1 − 1/e)-approximation algorithm for maximum bipartite matching in the price-of-information model introduced by Singla [25], who also used the basic greedy algorithm to give a (1/2)-approximation.

Connected through hyperedge HE-1 (induced by source paper "online stochastic max weight matching prophet inequality for vertex and edge arrival models"):
  with members:
    - "online stochastic matching with edge arrivals"
    - "optimal single choice prophet inequalities from samples"
    - "an optimal monotone contention resolution scheme for bipartite matchings via a polyhedral viewpoint"
    - "near optimal online algorithms and fast approximation algorithms for resource allocation problems"
    - "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"
    - "prophets and secretaries with overbooking"

Connected through hyperedge HE-2 (induced by source paper "online graph matching problems with a worst case reassignment budget"):
  with members:
    - "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"
    - "relaxing the irrevocability requirement for online graph algorithms"

Connected through hyperedge HE-3 (induced by source paper "a simple 1 1 e approximation for oblivious bipartite matching"):
  with members:
    - "perturbed greedy on oblivious matching problems"
    - "how to match when all vertices arrive online"
    - "the price of information in combinatorial optimization"
    - "the stochastic matching problem beating half with a non adaptive algorithm"
    - "online vertex weighted bipartite matching and single bid budgeted allocations"

Connected through hyperedge HE-4 (induced by source paper "towards a better understanding of randomized greedy matching"):
  with members:
    - "tight competitive ratios of classic matching algorithms in the fully online model"
    - "how to match when all vertices arrive online"
    - "matching with our eyes closed"

Connected through hyperedge HE-5 (induced by source paper "online stochastic matching with edge arrivals"):
  with members:
    - "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"
    - "buyback problem approximate matroid intersection with cancellation costs"

Connected through hyperedge HE-6 (induced by source paper "perturbed greedy on oblivious matching problems"):
  with members:
    - "improved approximation algorithms for stochastic 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)