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: "epsilon approximations and epsilon nets"
  Abstract: The use of random samples to approximate properties of geometric configurations has been an influential idea  for both combinatorial and algorithmic purposes. This chapter considers  two related notions---$\epsilon$-approximations and $\epsilon$-nets---that capture the most important quantitative properties that one would expect from a random sample with respect to an underlying geometric configuration.

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

Connected through hyperedge HE-2 (induced by source paper "algorithms for intersection graphs of multiple intervals and pseudo disks"):
  with members:
    - "on approximating partial set cover and generalizations"
    - "on pseudo disk hypergraphs"
    - "on partial covering for geometric set systems"
    - "approximation schemes for independent set and sparse subsets of polygons"
    - "approximation schemes for maximum weight independent set of rectangles"
    - "approximation algorithms for maximum independent set of pseudo disks"
    - "improved approximation algorithms for geometric set cover"
    - "a new multilayered pcp and the hardness of hypergraph vertex cover"

Connected through hyperedge HE-3 (induced by source paper "discrepancy coresets and sketches in machine learning"):
  with members:
    - "on coresets for logistic regression"
    - "near optimal coresets of kernel density estimates"
    - "improved coresets for kernel density estimates"
    - "optimal quantile approximation in streams"
    - "new frameworks for offline and streaming coreset constructions"
    - "a unified framework for approximating and clustering data"

Connected through hyperedge HE-4 (induced by source paper "when are epsilon nets small"):
  with members:
    - "the optimal sample complexity of pac learning"
    - "beyond disagreement based agnostic active learning"
    - "minimax analysis of active learning"
    - "active and passive learning of linear separators under log concave distributions"

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)