human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "combinatorial geometry of graph partitioning i"):
  Abstract: The {\sc $c$-Balanced Separator} problem is a graph-partitioning problem in which given a graph $G$, one aims to find a cut of minimum size such that both the sides of the cut have at least $cn$ vertices. In this paper, we present new directions of progress in the {\sc $c$-Balanced Separator} problem. More specifically, we propose a family of mathematical programs, that depend upon a parameter $p > 0$, and is an extension of the uniform version of the SDPs proposed by Goemans and Linial for this problem. In fact for the case, when $p=1$, if one can solve this program in polynomial time then simply using the Goemans-Williamson's randomized rounding algorithm for {\sc Max Cut} \cite{WG95} will give an $O(1)$-factor approximation algorithm for {\sc $c$-Balanced Separator} improving the best known approximation factor of $O(\sqrt{\log n})$ due to Arora, Rao and Vazirani \cite{ARV}. This family of programs is not convex but one can transform them into so called \emph{\textbf{concave programs}} in which one optimizes a concave function over a convex feasible set. It is well known that the optima of such programs lie at one of the extreme points of the feasible set \cite{TTT85}. Our main contribution is a combinatorial characterization of some extreme points of the feasible set of the mathematical program, for $p=1$ case, which to the best of our knowledge is the first of its kind. We further demonstrate how this characterization can be used to solve the program in a restricted setting. Non-convex programs have recently been investigated by Bhaskara and Vijayaraghvan \cite{BV11} in which they design algorithms for approximating Matrix $p$-norms although their algorithmic techniques are analytical in nature.

Includes members:
  Member "approximating matrix p norms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarially robust low dimensional representations")
  Member "measured descent a new embedding method for finite metrics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scattering and sparse partitions and their applications")

Question: Please tell me which class the source paper 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.DS(Data Structures and Algorithms)