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: "kidney exchange in dynamic sparse heterogenous pools"
  Abstract: Current kidney exchange pools are of moderate size and thin, as they consist of many highly sensitized patients. Creating a thicker pool can be done by waiting for many pairs to arrive. We analyze a simple class of matching algorithms that search periodically for allocations. We find that if only 2-way cycles are conducted, in order to gain a significant amount of matches over the online scenario (matching each time a new incompatible pair joins the pool) the waiting period should be "very long". If 3-way cycles are also allowed we find regimes in which waiting for a short period also increases the number of matches considerably. Finally, a significant increase of matches can be obtained by using even one non-simultaneous chain while still matching in an online fashion. Our theoretical findings and data-driven computational experiments lead to policy recommendations.

Connected through hyperedge HE-1 (induced by source paper "fair matching in dynamic kidney exchange"):
  with members:
    - "dynamic matching market design"

Connected through hyperedge HE-2 (induced by source paper "maximizing efficiency in dynamic matching markets"):
  with members:
    - "how to match when all vertices arrive online"
    - "online matching in a ride sharing platform"
    - "on matching and thickness in heterogeneous dynamic markets"
    - "online matching haste makes waste"
    - "online stochastic matching beating 1 1 e"

Connected through hyperedge HE-3 (induced by source paper "scalable robust kidney exchange"):
  with members:
    - "balancing lexicographic fairness and a utilitarian objective with application to kidney exchange"
    - "a voting based system for ethical decision making"
    - "robust optimization for non convex objectives"
    - "position indexed formulations for kidney exchange"

Connected through hyperedge HE-4 (induced by source paper "optimal control of dynamic bipartite matching models"):
  with members:
    - "stability of the bipartite matching model"
    - "online stochastic matching online actions based on offline statistics"
    - "online stochastic matching beating 1 1 e"

Connected through hyperedge HE-5 (induced by source paper "position indexed formulations for kidney exchange"):
  with members:
    - "ignorance is almost bliss near optimal stochastic matching with few queries"
    - "dynamic matching market design"

Connected through hyperedge HE-6 (induced by source paper "on matching and thickness in heterogeneous dynamic markets"):
  with members:
    - "online stochastic matching beating 1 1 e"

Connected through hyperedge HE-7 (induced by source paper "ignorance is almost bliss near optimal stochastic matching with few queries"):
  with members:
    - "dynamic matching market design"
    - "a stochastic probing problem with applications"
    - "matching with our eyes closed"
    - "when lp is the cure for your matching woes improved bounds for stochastic matchings"

Connected through hyperedge HE-8 (induced by source paper "dynamic matching market design"):
  with members:
    - "online stochastic matching online actions based on offline statistics"
    - "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)