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: "ranking on arbitrary graphs rematch via continuous lp with monotone and boundary condition constraints"
  Abstract: Motivated by online advertisement and exchange settings, greedy randomized algorithms for the maximum matching problem have been studied, in which the algorithm makes (random) decisions that are essentially oblivious to the input graph. Any greedy algorithm can achieve performance ratio 0.5, which is the expected number of matched nodes to the number of nodes in a maximum matching. #R##N#Since Aronson, Dyer, Frieze and Suen proved that the Modified Randomized Greedy (MRG) algorithm achieves performance ratio 0.5 + \epsilon (where \epsilon = frac{1}{400000}) on arbitrary graphs in the mid-nineties, no further attempts in the literature have been made to improve this theoretical ratio for arbitrary graphs until two papers were published in FOCS 2012. Poloczek and Szegedy also analyzed the MRG algorithm to give ratio 0.5039, while Goel and Tripathi used experimental techniques to analyze the Ranking algorithm to give ratio 0.56. However, we could not reproduce the experimental results of Goel and Tripathi. #R##N#In this paper, we revisit the Ranking algorithm using the LP framework. Special care is given to analyze the structural properties of the Ranking algorithm in order to derive the LP constraints, of which one known as the \emph{boundary} constraint requires totally new analysis and is crucial to the success of our LP. #R##N#We use continuous LP relaxation to analyze the limiting behavior as the finite LP grows. Of particular interest are new duality and complementary slackness characterizations that can handle the monotone and the boundary constraints in continuous LP. We believe our work achieves the currently best theoretical performance ratio of \frac{2(5-\sqrt{7})}{9} \approx 0.523 on arbitrary graphs. Moreover, experiments suggest that Ranking cannot perform better than 0.724 in general.

Connected through hyperedge HE-1 (induced by source paper "online vertex weighted bipartite matching beating 1 1 e with random arrivals"):
  with members:
    - "how to match when all vertices arrive online"
    - "new algorithms better bounds and a novel model for online stochastic matching"
    - "online vertex weighted bipartite matching and single bid budgeted allocations"
    - "online stochastic matching online actions based on offline statistics"
    - "online stochastic matching beating 1 1 e"

Connected through hyperedge HE-2 (induced by source paper "how to match when all vertices arrive online"):
  with members:
    - "on randomized algorithms for matching in the online preemptive model"
    - "improved bounds for online preemptive matching"
    - "buyback problem approximate matroid intersection with cancellation costs"
    - "online vertex weighted bipartite matching and single bid budgeted allocations"

Connected through hyperedge HE-3 (induced by source paper "the stochastic matching problem beating half with a non adaptive algorithm"):
  with members:
    - "ignorance is almost bliss near optimal stochastic matching with few queries"
    - "dynamic matching market design"
    - "a stochastic probing problem with applications"
    - "maximum matching in semi streaming with few passes"
    - "when lp is the cure for your matching woes improved bounds for stochastic matchings"

Connected through hyperedge HE-4 (induced by source paper "online matroid intersection beating half for random arrival"):
  with members:
    - "online submodular welfare maximization greedy beats 1 2 in random order"
    - "a faster cutting plane method and its implications for combinatorial and convex optimization"
    - "o log log rank competitive ratio for the matroid secretary problem"
    - "a simple o log log mathrm rank competitive algorithm for the matroid secretary problem"
    - "better bounds for matchings in the streaming model"
    - "matroid prophet inequalities"
    - "improved bounds for online preemptive matching"
    - "maximum matching in semi streaming with few passes"

Connected through hyperedge HE-5 (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"
    - "matching with our eyes closed"

Connected through hyperedge HE-6 (induced by source paper "on the approximation performance of degree heuristics for matching"):
  with members:
    - "approximation bounds for minimum degree matching"
    - "matching with our eyes closed"
    - "the query commit problem"

Connected through hyperedge HE-7 (induced by source paper "on randomized algorithms for matching in the online preemptive model"):
  with members:
    - "improved bounds for online preemptive matching"
    - "buyback problem approximate matroid intersection with cancellation costs"

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)