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: "a size sensitive discrepancy bound for set systems of bounded primal shatter dimension"
  Abstract: Let $(X,\S)$ be a set system on an $n$-point set $X$. The \emph{discrepancy} of $\S$ is defined as the minimum of the largest deviation from an even split, over all subsets of $S \in \S$ and two-colorings $\chi$ on $X$. We consider the scenario where, for any subset $X' \subseteq X$ of size $m \le n$ and for any parameter $1 \le k \le m$, the number of restrictions of the sets of $\S$ to $X'$ of size at most $k$ is only $O(m^{d_1} k^{d-d_1})$, for fixed integers $d > 0$ and $1 \le d_1 \le d$ (this generalizes the standard notion of \emph{bounded primal shatter dimension} when $d_1 = d$). In this case we show that there exists a coloring $\chi$ with discrepancy bound $O^{*}(|S|^{1/2 - d_1/(2d)} n^{(d_1 - 1)/(2d)})$, for each $S \in \S$, where $O^{*}(\cdot)$ hides a polylogarithmic factor in $n$. This bound is tight up to a polylogarithmic factor \cite{Mat-95, Mat-99} and the corresponding coloring $\chi$ can be computed in expected polynomial time using the very recent machinery of Lovett and Meka for constructive discrepancy minimization \cite{LM-12}. Our bound improves and generalizes the bounds obtained from the machinery of Har-Peled and Sharir \cite{HS-11} (and the follow-up work in \cite{SZ-12}) for points and halfspaces in $d$-space for $d \ge 3$.

Connected through hyperedge HE-1 (induced by source paper "epsilon approximations and epsilon nets"):
  with members:
    - "near optimal epsilon kernel construction and related problems"
    - "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"
    - "algorithms for eps approximations of terrains"
    - "improved approximation algorithms for geometric set cover"

Connected through hyperedge HE-2 (induced by source paper "efficient algorithms for discrepancy minimization in convex sets"):
  with members:
    - "constructive discrepancy minimization for convex sets"
    - "approximating bin packing within o log opt log log opt bins"
    - "the geometry of differential privacy the sparse and approximate cases"
    - "constructive discrepancy minimization by walking on the edges"
    - "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)