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 matching guarantees and limitations"
  Abstract: Since Tinhofer proposed the MinGreedy algorithm for maximum cardinality matching in 1984, several experimental studies found the randomized algorithm to perform excellently for various classes of random graphs and benchmark instances. In contrast, only few analytical results are known. We show that MinGreedy cannot improve on the trivial approximation ratio 1/2 whp., even for bipartite graphs. Our hard inputs seem to require a small number of high-degree nodes. #R##N#This motivates an investigation of greedy algorithms on graphs with maximum degree D: We show that MinGreedy achieves a (D-1)/(2D-3)-approximation for graphs with D=3 and for D-regular graphs, and a guarantee of (D-1/2)/(2D-2) for graphs with maximum degree D. Interestingly, our bounds even hold for the deterministic MinGreedy that breaks all ties arbitrarily. #R##N#Moreover, we investigate the limitations of the greedy paradigm, using the model of priority algorithms introduced by Borodin, Nielsen, and Rackoff. We study deterministic priority algorithms and prove a (D-1)/(2D-3)-inapproximability result for graphs with maximum degree D; thus, these greedy algorithms do not achieve a 1/2+eps-approximation and in particular the 2/3-approximation obtained by the deterministic MinGreedy for D=3 is optimal in this class. For k-uniform hypergraphs we show a tight 1/k-inapproximability bound. We also study fully randomized priority algorithms and give a 5/6-inapproximability bound. Thus, they cannot compete with matching algorithms of other paradigms.

Connected through hyperedge HE-1 (induced by source paper "advice complexity of adaptive priority algorithms"):
  with members:
    - "advice complexity of priority algorithms"
    - "deterministic graph exploration with advice"
    - "advice complexity of the online induced subgraph problem"
    - "an o 1 0821 n time algorithm for computing maximum independent set in graphs with bounded degree 3"

Connected through hyperedge HE-2 (induced by source paper "advice complexity of priority algorithms"):
  with members:
    - "on the limitations of deterministic de randomizations for online bipartite matching and max sat"
    - "on the power of advice and randomization for online bipartite matching"
    - "randomization can be as helpful as a glimpse of the future in online computation"
    - "online bin packing with advice"

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

Connected through hyperedge HE-4 (induced by source paper "on conceptually simple algorithms for variants of online bipartite matching"):
  with members:
    - "an experimental study of algorithms for online bipartite matching"
    - "on the limitations of deterministic de randomizations for online bipartite matching and max sat"
    - "computing maximum flow with augmenting electrical flows"
    - "on the power of advice and randomization for online bipartite matching"
    - "new algorithms better bounds and a novel model for online stochastic matching"
    - "deterministic algorithms for submodular maximization problems"
    - "greedy algorithms make efficient mechanisms"
    - "matchings with lower quotas algorithms and complexity"

Connected through hyperedge HE-5 (induced by source paper "on the limitations of deterministic de randomizations for online bipartite matching and max sat"):
  with members:
    - "on the power of advice and randomization for online bipartite matching"
    - "randomization can be as helpful as a glimpse of the future in online computation"
    - "deterministic algorithms for submodular maximization problems"
    - "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)