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: "linear time approximation schemes for geometric maximum coverage"
  Abstract: We study approximation algorithms for the following geometric version of the maximum coverage problem: Let \(\mathcal {P}\) be a set of n weighted points in the plane. We want to place m \(a \times b\) rectangles such that the sum of the weights of the points in \(\mathcal {P}\) covered by these rectangles is maximized. For any fixed \(\varepsilon >0\), we present efficient approximation schemes that can find a \((1-\varepsilon )\)-approximation to the optimal solution. In particular, for \(m=1\), our algorithm runs in linear time \(O(n\log (\frac{1}{\varepsilon }))\), improving over the previous result. For \(m>1\), we present an algorithm that runs in \(O(\frac{n}{\varepsilon }\log (\frac{1}{\varepsilon })+m(\frac{1}{\varepsilon })^{O(\min (\sqrt{m},\frac{1}{\varepsilon }))})\) time.

Connected through hyperedge HE-1 (induced by source paper "similarity caching theory and algorithms"):
  with members:
    - "a swiss army knife for dynamic caching in small cell networks"
    - "on the convergence of the ttl approximation for an lru cache under independent stationary request processes"
    - "clipper a low latency online prediction serving system"
    - "neural turing machines"
    - "a versatile and accurate approximation for lru cache performance"

Connected through hyperedge HE-2 (induced by source paper "construction and maintenance of swarm drones"):
  with members:
    - "efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage"
    - "wireless communications with unmanned aerial vehicles opportunities and challenges"
    - "scheduling with gaps new models and algorithms"
    - "covering points by disjoint boxes with outliers"

Connected through hyperedge HE-3 (induced by source paper "partial sublinear time approximation and inapproximation for maximum coverage"):
  with members:
    - "on the closest string and substring 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)