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: "revisiting connected vertex cover fpt algorithms and lossy kernels"
  Abstract: The Connected Vertex Cover problem asks for a vertex cover in a graph that induces a connected subgraph. The problem is known to be fixed-parameter tractable (FPT), and is unlikely to have a polynomial sized kernel (under complexity theoretic assumptions) when parameterized by the solution size. In a recent paper, Lokshtanov et al. [STOC 2017], have shown an α-approximate kernel for the problem for every α > 1, in the framework of approximate or lossy kernelization. In this work, we exhibit lossy kernels and FPT algorithms for Connected Vertex Cover for parameters that are more natural and functions of the input, and in some cases, smaller than the solution size. Our first result is a lossy kernel for Connected Vertex Cover parameterized by the size of a split deletion set. Let n denote the number of vertices in the input graph. We show that #R##N##R##N##R##N#Connected Vertex Cover parameterized by the size k of a split deletion set admits an α-approximate kernel with \(\mathcal {O}(k+(2k+\lceil \frac {2\alpha -1}{\alpha -1} \rceil )^{\lceil \frac {2\alpha -1}{\alpha -1} \rceil })\) vertices and an algorithm with \(\mathcal {O}(3^{k} n^{\mathcal {O}(1)})\) running time.#R##N##R##N##R##N#For the special case when the split deletion set is a clique deletion set, the algorithm runs in \({\mathcal O}(2^{k} n^{{\mathcal O}(1)})\) time and the lossy kernel has \(\mathcal {O}(k + \lceil \frac {2\alpha -1}{\alpha -1} \rceil )\) vertices.

Connected through hyperedge HE-1 (induced by source paper "on the approximate compressibility of connected vertex cover"):
  with members:
    - "smaller parameters for vertex cover kernelization"
    - "approximation and kernelization for chordal vertex deletion"
    - "how much does a treedepth modulator help to obtain polynomial kernels beyond sparse graphs"
    - "a randomized polynomial kernelization for vertex cover with a smaller parameter"
    - "vertex cover structural parameterization revisited"
    - "kernelization lower bounds by cross composition"
    - "kernelization of packing problems"
    - "representative sets and irrelevant vertices new tools for kernelization"

Connected through hyperedge HE-2 (induced by source paper "on approximate data reduction for the rural postman problem theory and experiments"):
  with members:
    - "parameterized algorithms and data reduction for the short secluded s t path problem"
    - "a parameterized approximation algorithm for the mixed and windy capacitated arc routing problem theory and experiments"
    - "polynomial kernels for weighted problems"
    - "constant factor approximations for capacitated arc routing without triangle inequality"
    - "fixed parameter algorithms for minimum cost edge connectivity augmentation"
    - "new inapproximability bounds for tsp"

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)