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: "matching with our eyes closed"
  Abstract: Motivated by an application in kidney exchange, we study the following query-commit problem: we are given the set of vertices of a non-bipartite graph G. The set of edges in this graph are not known ahead of time. We can query any pair of vertices to determine if they are adjacent. If the queried edge exists, we are committed to match the two endpoints. Our objective is to maximize the size of the matching. #R##N#This restriction in the amount of information available to the algorithm constraints us to implement myopic, greedy-like algorithms. A simple deterministic greedy algorithm achieves a factor 1/2 which is tight for deterministic algorithms. An important open question in this direction is to give a randomized greedy algorithm that has a significantly better approximation factor. This question was first asked almost 20 years ago by Dyer and Frieze [9] where they showed that a natural randomized strategy of picking edges uniformly at random doesn't help and has an approximation factor of 1/2 + o(1). They left it as an open question to devise a better randomized greedy algorithm. In subsequent work, Aronson, Dyer, Frieze, and Suen [2] gave a different randomized greedy algorithm and showed that it attains a factor 0.5 + epsilon where epsilon is 0.0000025. #R##N#In this paper we propose and analyze a new randomized greedy algorithm for finding a large matching in a general graph and use it to solve the query commit problem mentioned above. We show that our algorithm attains a factor of at least 0.56, a significant improvement over 0.50000025. We also show that no randomized algorithm can have an approximation factor better than 0.7916 for the query commit problem. For another large and interesting class of randomized algorithms that we call vertex-iterative algorithms, we show that no vertex-iterative algorithm can have an approximation factor better than 0.75.

Connected through hyperedge HE-1 (induced by source paper "towards a better understanding of randomized greedy matching"):
  with members:
    - "beating greedy for stochastic bipartite matching"
    - "tight competitive ratios of classic matching algorithms in the fully online model"
    - "how to match when all vertices arrive online"

Connected through hyperedge HE-2 (induced by source paper "opting into optimal matchings"):
  with members:
    - "ignorance is almost bliss near optimal stochastic matching with few queries"
    - "a stochastic probing problem with applications"
    - "when lp is the cure for your matching woes improved bounds for stochastic matchings"
    - "a note on concentration of submodular functions"

Connected through hyperedge HE-3 (induced by source paper "greedy matching guarantees and limitations"):
  with members:
    - "set merging for the matching algorithm of micali and vazirani"
    - "on the approximation performance of degree heuristics for matching"
    - "a simplication of the mv matching algorithm and its proof"
    - "improved approximation for 3 dimensional matching via bounded pathwidth local search"
    - "ranking on arbitrary graphs rematch via continuous lp with monotone and boundary condition constraints"

Connected through hyperedge HE-4 (induced by source paper "ignorance is almost bliss near optimal stochastic matching with few queries"):
  with members:
    - "dynamic matching market design"
    - "kidney exchange in dynamic sparse heterogenous pools"
    - "a stochastic probing problem with applications"
    - "when lp is the cure for your matching woes improved bounds for stochastic matchings"

Connected through hyperedge HE-5 (induced by source paper "on the approximation performance of degree heuristics for matching"):
  with members:
    - "approximation bounds for minimum degree matching"
    - "ranking on arbitrary graphs rematch via continuous lp with monotone and boundary condition constraints"
    - "the query commit problem"

Connected through hyperedge HE-6 (induced by source paper "online bipartite matching with decomposable weights"):
  with members:
    - "improved bounds for online preemptive matching"
    - "buyback problem approximate matroid intersection with cancellation costs"
    - "online vertex weighted bipartite matching and single bid budgeted allocations"
    - "a dynamic near optimal algorithm for online linear programming"
    - "randomized online algorithms for the buyback problem"
    - "online stochastic matching beating 1 1 e"

Connected through hyperedge HE-7 (induced by source paper "online vertex cover and matching beating the greedy algorithm"):
  with members:
    - "when lp is the cure for your matching woes improved bounds for stochastic matchings"
    - "online vertex weighted bipartite matching and single bid budgeted allocations"
    - "online stochastic matching beating 1 1 e"
    - "rent lease or buy randomized algorithms for multislope ski rental"

Connected through hyperedge HE-8 (induced by source paper "ranking on arbitrary graphs rematch via continuous lp with monotone and boundary condition constraints"):
  with members:
    - "online vertex weighted bipartite matching and single bid budgeted allocations"

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)