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: "greedy online bipartite matching on random graphs"
  Abstract: We study the average performance of online greedy matching algorithms on G(n,n,p), the random bipartite graph with n vertices on each side and edges occurring independently with probability p = p(n). In the online model, vertices on one side of the graph are given up front while vertices on the other side arrive sequentially; when a vertex arrives its edges are revealed and it must be immediately matched or dropped. We begin by analyzing the oblivious algorithm, which tries to match each arriving vertex to a random neighbor, even if the neighbor has already been matched. The algorithm is shown to have a performance ratio of at least 1 − 1/e for all monotonic functions p(n), where the performance ratio is defined asymptotically as the ratio of the expected matching size given by the algorithm to the expected maximum matching size. Next we show that the conventional greedy algorithm, which assigns each vertex to a random unmatched neighbor, has a performance ratio of at least 0.837 for all monotonic functions p(n). Under the G(n,n,p) model, the performance of greedy is equivalent to the performance of the well known ranking algorithm, so our results show that ranking has a performance ratio of at least 0.837. We finally consider vertex-weighted bipartite matching. Our proofs are based on simple differential equations that describe the evolution of the matching process.

Connected through hyperedge HE-1 (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"
    - "when lp is the cure for your matching woes improved bounds for stochastic matchings"

Connected through hyperedge HE-2 (induced by source paper "an experimental study of algorithms for online bipartite matching"):
  with members:
    - "on conceptually simple algorithms for variants of online bipartite matching"
    - "on the power of advice and randomization for online bipartite matching"
    - "are stable instances easy"
    - "online stochastic matching beating 1 1 e"

Connected through hyperedge HE-3 (induced by source paper "greedy bipartite matching in random type poisson arrival model"):
  with members:
    - "greedy matching guarantees and limitations"
    - "online stochastic matching beating 1 1 e"

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)