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: "perturbed greedy on oblivious matching problems"
  Abstract: We study the maximum matching problem in the oblivious setting, i.e. the edge set of the graph is unknown to the algorithm. The existence of an edge is revealed upon the probe of this pair of vertices. Further, if an edge exists between two probed vertices, then the edge must be included in the matching irrevocably. For unweighted graphs, the \textsf{Ranking} algorithm by Karp et al.~(STOC 1990) achieves approximation ratios $0.696$ for bipartite graphs and $0.526$ for general graphs. For vertex-weighted graphs, Chan et al. (TALG 2018) proposed a $0.501$-approximate algorithm. In contrast, the edge-weighted version only admits the trivial $0.5$-approximation by Greedy. #R##N#In this paper, we propose the \textsf{Perturbed Greedy} algorithm for the edge-weighted oblivious matching problem and prove that it achieves a $0.501$ approximation ratio. Besides, we show that the approximation ratio of our algorithm on unweighted graphs is $0.639$ for bipartite graphs, and $0.531$ for general graphs. The later improves the state-of-the-art result by Chan et al. (TALG 2018). Furthermore, our algorithm can be regarded as a robust version of the \textsf{Modified Randomized Greedy} (MRG) algorithm. By implication, our $0.531$ approximation ratio serves as the first analysis of the MRG algorithm beyond the $(1/2+\epsilon)$ regime.

Connected through hyperedge HE-1 (induced by source paper "a simple 1 1 e approximation for oblivious bipartite matching"):
  with members:
    - "beating greedy for stochastic bipartite matching"
    - "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"

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)