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: "tight lower bounds for the size of epsilon nets"
  Abstract: According to a well known theorem of Haussler and Welzl (1987), any range space of bounded VC-dimension admits an $\eps$-net of size $O\left(\frac{1}{\eps}\log\frac1{\eps}\right)$. Using probabilistic techniques, Pach and Woeginger (1990) showed that there exist range spaces of VC-dimension 2, for which the above bound can be attained. The only known range spaces of small VC-dimension, in which the ranges are geometric objects in some Euclidean space and the size of the smallest $\eps$-nets is superlinear in $\frac1{\eps}$, were found by Alon (2010). In his examples, the size of the smallest $\eps$-nets is $\Omega\left(\frac{1}{\eps}g(\frac{1}{\eps})\right)$, where $g$ is an extremely slowly growing function, closely related to the inverse Ackermann function. #R##N#\smallskip #R##N#We show that there exist geometrically defined range spaces, already of VC-dimension $2$, in which the size of the smallest $\eps$-nets is $\Omega\left(\frac{1}{\eps}\log\frac{1}{\eps}\right)$. We also construct range spaces induced by axis-parallel rectangles in the plane, in which the size of the smallest $\eps$-nets is $\Omega\left(\frac{1}{\eps}\log\log\frac{1}{\eps}\right)$. By a theorem of Aronov, Ezra, and Sharir (2010), this bound is tight.

Connected through hyperedge HE-1 (induced by source paper "the epsilon t net problem"):
  with members:
    - "epsilon approximations and epsilon nets"
    - "improved approximation algorithms for geometric set cover"

Connected through hyperedge HE-2 (induced by source paper "distribution sensitive bounds on relative approximations of geometric ranges"):
  with members:
    - "when are epsilon nets small"

Connected through hyperedge HE-3 (induced by source paper "optimal bounds on the vc dimension"):
  with members:
    - "decomposing arrangements of hyperplanes vc dimension combinatorial dimension and point location"
    - "distributed k means and k median clustering on general topologies"
    - "a unified framework for approximating and clustering data"

Connected through hyperedge HE-4 (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"
    - "improved approximation for guarding simple galleries from the perimeter"
    - "algorithms for eps approximations of terrains"
    - "improved approximation algorithms for geometric set cover"

Connected through hyperedge HE-5 (induced by source paper "coresets and sketches"):
  with members:
    - "coresets for k means and k median clustering and their applications"
    - "optimal quantile approximation in streams"
    - "dimensionality reduction of massive sparse datasets using coresets"
    - "the optimal sample complexity of pac learning"
    - "input sparsity and hardness for robust subspace approximation"
    - "sparse approximation via generating point sets"
    - "frequent directions simple and deterministic matrix sketching"
    - "epsilon kernel coresets for stochastic points"

Connected through hyperedge HE-6 (induced by source paper "geometric hitting set for segments of few orientations"):
  with members:
    - "fixed parameter tractability and lower bounds for stabbing problems"
    - "on the approximability of covering points by lines and related problems"
    - "point line cover the easy kernel is essentially tight"
    - "analytical approach to parallel repetition"
    - "improved approximation algorithms for geometric set cover"

Connected through hyperedge HE-7 (induced by source paper "on kinetic range spaces and their applications"):
  with members:
    - "interference minimization in asymmetric sensor networks"
    - "minimizing interference in ad hoc networks with bounded communication radius"
    - "improved bounds and new techniques for davenport schinzel sequences and their generalizations"

Connected through hyperedge HE-8 (induced by source paper "subsampling in smoothed range spaces"):
  with members:
    - "epsilon nets for halfspaces revisited"
    - "algorithms for eps approximations of terrains"

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.DM(Discrete Mathematics)