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: "on the largest empty axis parallel box amidst n points"
  Abstract: We give the first nontrivial upper and lower bounds on the maximum volume of an empty axis-parallel box inside an axis-parallel unit hypercube in $\RR^d$ containing $n$ points. For a fixed $d$, we show that the maximum volume is of the order $\Theta(\frac{1}{n})$. We then use the fact that the maximum volume is $\Omega(\frac{1}{n})$ in our design of the first efficient $(1-\eps)$-approximation algorithm for the following problem: Given an axis-parallel $d$-dimensional box $R$ in $\RR^d$ containing $n$ points, compute a maximum-volume empty axis-parallel $d$-dimensional box contained in $R$. The running time of our algorithm is nearly linear in $n$, for small $d$, and increases only by an $O(\log{n})$ factor when one goes up one dimension. No previous efficient exact or approximation algorithms were known for this problem for $d \geq 4$. As the problem has been recently shown to be NP-hard in arbitrary high dimensions (i.e., when $d$ is part of the input), the existence of efficient exact algorithms is unlikely. #R##N#We also obtain tight estimates on the maximum volume of an empty axis-parallel hypercube inside an axis-parallel unit hypercube in $\RR^d$ containing $n$ points. For a fixed $d$, this maximum volume is of the same order order $\Theta(\frac{1}{n})$. A faster $(1-\eps)$-approximation algorithm, with a milder dependence on $d$ in the running time, is obtained in this case.

Connected through hyperedge HE-1 (induced by source paper "deterministic constructions of high dimensional sets with small dispersion"):
  with members:
    - "on the dispersion of sparse grids"
    - "a lower bound for the dispersion on the torus"
    - "on the size of the largest empty box amidst a point set"

Connected through hyperedge HE-2 (induced by source paper "an upper bound of the minimal dispersion via delta covers"):
  with members:
    - "a lower bound for the dispersion on the torus"
    - "on the size of the largest empty box amidst a point set"

Connected through hyperedge HE-3 (induced by source paper "a note on the dispersion of admissible lattices"):
  with members:
    - "a note on minimal dispersion of point sets in the unit cube"
    - "on the dispersion of sparse grids"
    - "a lower bound for the dispersion on the torus"
    - "on the size of the largest empty box amidst a point set"

Connected through hyperedge HE-4 (induced by source paper "a note on minimal dispersion of point sets in the unit cube"):
  with members:
    - "an upper bound of the minimal dispersion via delta covers"
    - "on the size of the largest empty box amidst a point set"

Connected through hyperedge HE-5 (induced by source paper "maximum area rectangle separating red and blue points"):
  with members:
    - "finding the maximal empty rectangle containing a query point"

Connected through hyperedge HE-6 (induced by source paper "on the dispersion of sparse grids"):
  with members:
    - "on the size of the largest empty box amidst a point set"

Connected through hyperedge HE-7 (induced by source paper "anchored rectangle and square packings"):
  with members:
    - "approximation schemes for independent set and sparse subsets of polygons"
    - "on approximating maximum independent set of rectangles"
    - "a constant factor approximation for multi covering with disks"
    - "quasi polynomial time approximation scheme for sparse subsets of polygons"
    - "a qptas for maximum weight independent set of polygons with polylogarithmically many vertices"
    - "approximation schemes for maximum weight independent set of rectangles"
    - "approximation algorithms for maximum independent set of pseudo disks"
    - "the problem of compatible representatives"

Connected through hyperedge HE-8 (induced by source paper "big holes in big data a monte carlo algorithm for detecting large hyper rectangles in high dimensional data"):
  with members:
    - "joins via geometric resolutions worst case and beyond"

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)