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: "max sum diversity via convex programming"
  Abstract: Diversity maximization is an important concept in information retrieval, computational geometry and operations research. Usually, it is a variant of the following problem: Given a ground set, constraints, and a function $f(\cdot)$ that measures diversity of a subset, the task is to select a feasible subset $S$ such that $f(S)$ is maximized. The \emph{sum-dispersion} function $f(S) = \sum_{x,y \in S} d(x,y)$, which is the sum of the pairwise distances in $S$, is in this context a prominent diversification measure. The corresponding diversity maximization is the \emph{max-sum} or \emph{sum-sum diversification}. Many recent results deal with the design of constant-factor approximation algorithms of diversification problems involving sum-dispersion function under a matroid constraint. In this paper, we present a PTAS for the max-sum diversification problem under a matroid constraint for distances $d(\cdot,\cdot)$ of \emph{negative type}. Distances of negative type are, for example, metric distances stemming from the $\ell_2$ and $\ell_1$ norm, as well as the cosine or spherical, or Jaccard distance which are popular similarity metrics in web and image search.

Connected through hyperedge HE-1 (induced by source paper "beyond submodular maximization"):
  with members:
    - "distributed maximization of submodular plus diversity functions for multi label feature selection on huge datasets"
    - "weakly submodular maximization beyond cardinality constraints does randomization help greedy"
    - "online submodular welfare maximization greedy beats 1 2 in random order"
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "local search for max sum diversification"
    - "max sum diversification monotone submodular functions and semi metric spaces"
    - "building a good team secretary problems and the supermodular degree"
    - "a new framework for distributed submodular maximization"

Connected through hyperedge HE-2 (induced by source paper "mapreduce and streaming algorithms for diversity maximization in metric spaces of bounded doubling dimension"):
  with members:
    - "the traveling salesman problem low dimensionality implies a polynomial time approximation scheme"
    - "a practical parallel algorithm for diameter approximation of massive weighted graphs"
    - "space and time efficient parallel graph decomposition clustering and diameter approximation"
    - "efficient classification for metric data"
    - "space round tradeoffs for mapreduce computations"
    - "maximum dispersion and geometric maximum weight cliques"

Connected through hyperedge HE-3 (induced by source paper "local search for max sum diversification"):
  with members:
    - "mapreduce and streaming algorithms for diversity maximization in metric spaces of bounded doubling dimension"
    - "randomized rounding for the largest simplex problem"
    - "optimal approximation for submodular and supermodular optimization with bounded curvature"
    - "max sum diversification monotone submodular functions and dynamic updates"
    - "maximum dispersion and geometric maximum weight cliques"

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.DS(Data Structures and Algorithms)