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: "geometric dominating set and set cover via local search"
  Abstract: In this paper, we study two classic optimization problems: minimum geometric dominating set and set cover. Both the problems have been studied for different types of objects for a long time. These problems become APX-hard when the objects are axis-parallel rectangles, ellipses, $\alpha$-fat objects of constant description complexity, and convex polygons. On the other hand, PTAS (polynomial time approximation scheme) is known for them when the objects are disks or unit squares. Surprisingly, PTAS was unknown even for arbitrary squares. For homothetic set of convex objects, an $O(k^4)$ approximation algorithm is known for dominating set problem, where $k$ is the number of corners in a convex object. On the other hand, QPTAS (quasi polynomial time approximation scheme) is known very recently for the covering problem when the objects are pseudodisks. For both problems obtaining a PTAS remains open for a large class of objects. #R##N#For the dominating set problems, we prove that the popular local search algorithm leads to an $(1+\varepsilon)$ approximation when objects are homothetic set of convex objects (which includes arbitrary squares, $k$-regular polygons, translated and scaled copies of a convex set etc.) in $n^{O(1/\varepsilon^2)}$ time. On the other hand, the same technique leads to a PTAS for geometric covering problem when the objects are convex pseudodisks (which includes disks, unit height rectangles, homothetic convex objects etc.). As a consequence, we obtain an easy to implement approximation algorithm for both problems for a large class of objects, significantly improving the best known approximation guarantees.

Connected through hyperedge HE-1 (induced by source paper "approximating dominating set on intersection graphs of rectangles and l frames"):
  with members:
    - "max point tolerance graphs"
    - "the hardness of approximation of euclidean k means"
    - "independent and hitting sets of rectangles intersecting a diagonal line algorithms and complexity"
    - "analytical approach to parallel repetition"
    - "approximation algorithms for maximum independent set of pseudo disks"

Connected through hyperedge HE-2 (induced by source paper "approximation schemes for geometric coverage problems"):
  with members:
    - "structured recursive separator decompositions for planar graphs in linear time"
    - "approximation algorithms for maximum independent set of pseudo disks"
    - "approximating low dimensional coverage problems"

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.CG(Computational Geometry)