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: "algorithms for eps approximations of terrains"
  Abstract: Consider a point set D with a measure function w : D -> R. Let A be the set of subsets of D induced by containment in a shape from some geometric family (e.g. axis-aligned rectangles, half planes, balls, k-oriented polygons). We say a range space (D, A) has an eps-approximation P if max {R \in A} | w(R \cap P)/w(P) - w(R \cap D)/w(D) | <= eps. We describe algorithms for deterministically constructing discrete eps-approximations for continuous point sets such as distributions or terrains. Furthermore, for certain families of subsets A, such as those described by axis-aligned rectangles, we reduce the size of the eps-approximations by almost a square root from O(1/eps^2 log 1/eps) to O(1/eps polylog 1/eps). This is often the first step in transforming a continuous problem into a discrete one for which combinatorial techniques can be applied. We describe applications of this result in geo-spatial analysis, biosurveillance, and sensor networks.

Connected through hyperedge HE-1 (induced by source paper "near optimal coresets of kernel density estimates"):
  with members:
    - "improved coresets for kernel density estimates"
    - "the gram schmidt walk a cure for the banaszczyk blues"
    - "geometric inference on kernel density estimates"
    - "on the equivalence between herding and conditional gradient algorithms"
    - "a gentle introduction to the kernel distance"

Connected through hyperedge HE-2 (induced by source paper "epsilon approximations and epsilon nets"):
  with members:
    - "near optimal epsilon kernel construction and related problems"
    - "a size sensitive discrepancy bound for set systems of bounded primal shatter dimension"
    - "small strong epsilon nets"
    - "constructive discrepancy minimization by walking on the edges"
    - "a unified framework for approximating and clustering data"
    - "tight lower bounds for the size of epsilon nets"
    - "improved approximation for guarding simple galleries from the perimeter"
    - "improved approximation algorithms for geometric set cover"

Connected through hyperedge HE-3 (induced by source paper "range clustering queries"):
  with members:
    - "the 2 center problem in three dimensions"

Connected through hyperedge HE-4 (induced by source paper "covering many points with a small area box"):
  with members:
    - "net and prune a linear time algorithm for euclidean distance problems"
    - "covering points by disjoint boxes with outliers"

Connected through hyperedge HE-5 (induced by source paper "subsampling in smoothed range spaces"):
  with members:
    - "epsilon nets for halfspaces revisited"
    - "tight lower bounds for the size of epsilon nets"

Connected through hyperedge HE-6 (induced by source paper "range counting coresets for uncertain data"):
  with members:
    - "geometric computations on indecisive and uncertain points"
    - "shape fitting on point sets with probability distributions"
    - "histograms and wavelets on probabilistic data"

Connected through hyperedge HE-7 (induced by source paper "geometric computations on indecisive and uncertain points"):
  with members:
    - "constructive discrepancy minimization by walking on the edges"
    - "constructive algorithms for discrepancy minimization"

Connected through hyperedge HE-8 (induced by source paper "epsilon samples of kernels"):
  with members:
    - "constructive discrepancy minimization by walking on the edges"
    - "structure aware sampling flexible and accurate summarization"
    - "the vc dimension of queries and selectivity estimation through sampling"
    - "constructive algorithms for discrepancy minimization"

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)