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: "fast exact k means k medians and bregman divergence clustering in 1d"
  Abstract: The $k$-Means clustering problem on $n$ points is NP-Hard for any dimension $d\ge 2$, however, for the 1D case there exists exact polynomial time algorithms. Previous literature reported an $O(kn^2)$ time dynamic programming algorithm that uses $O(kn)$ space. It turns out that the problem has been considered under a different name more than twenty years ago. We present all the existing work that had been overlooked and compare the various solutions theoretically. Moreover, we show how to reduce the space usage for some of them, as well as generalize them to data structures that can quickly report an optimal $k$-Means clustering for any $k$. Finally we also generalize all the algorithms to work for the absolute distance and to work for any Bregman Divergence. We complement our theoretical contributions by experiments that compare the practical performance of the various algorithms.

Connected through hyperedge HE-1 (induced by source paper "polynomial algorithms for p dispersion problems in a 2d pareto front"):
  with members:
    - "faster k medoids clustering improving the pam clara and clarans algorithms"
    - "maximum volume subset selection for anchored boxes"

Connected through hyperedge HE-2 (induced by source paper "planar p center problems are solvable in polynomial time when clustering a pareto front"):
  with members:
    - "clustering in a 2 dimensional pareto front the p median and p center problems are polynomially solvable"
    - "maximum volume subset selection for anchored boxes"
    - "computing k centers on a line"

Connected through hyperedge HE-3 (induced by source paper "monte carlo information geometry the dually flat case"):
  with members:
    - "on w mixtures finite convex combinations of prescribed component distributions"
    - "guaranteed bounds on information theoretic measures of univariate mixtures using piecewise log sum exp inequalities"
    - "optimal interval clustering application to bregman clustering and statistical mixture learning"
    - "the burbea rao and bhattacharyya centroids"
    - "a family of statistical symmetric divergences based on jensen s inequality"
    - "statistical exponential families a digest with flash cards"

Connected through hyperedge HE-4 (induced by source paper "clustering in a 2 dimensional pareto front the p median and p center problems are polynomially solvable"):
  with members:
    - "maximum volume subset selection for anchored boxes"
    - "optimal interval clustering application to bregman clustering and statistical mixture learning"

Connected through hyperedge HE-5 (induced by source paper "on the fine grained complexity of one dimensional dynamic programming"):
  with members:
    - "fine grained complexity analysis of two classic tsp variants"
    - "a near linear pseudopolynomial time algorithm for subset sum"
    - "clustered integer 3sum via additive combinatorics"
    - "simulating branching programs with edit distance and friends or a polylog shaved is a lower bound made"
    - "a faster pseudopolynomial time algorithm for subset sum"
    - "quadratic conditional lower bounds for string problems and dynamic time warping"
    - "why walking the dog takes time frechet distance has no strongly subquadratic algorithms unless seth fails"
    - "edit distance cannot be computed in strongly subquadratic time unless seth is false"

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)