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: "capacitated center problems with two sided bounds and outliers"
  Abstract: In recent years, the capacitated center problems have attracted a lot of research interest. Given a set of vertices $V$, we want to find a subset of vertices $S$, called centers, such that the maximum cluster radius is minimized. Moreover, each center in $S$ should satisfy some capacity constraint, which could be an upper or lower bound on the number of vertices it can serve. Capacitated $k$-center problems with one-sided bounds (upper or lower) have been well studied in previous work, and a constant factor approximation was obtained. #R##N#We are the first to study the capacitated center problem with both capacity lower and upper bounds (with or without outliers). We assume each vertex has a uniform lower bound and a non-uniform upper bound. For the case of opening exactly $k$ centers, we note that a generalization of a recent LP approach can achieve constant factor approximation algorithms for our problems. Our main contribution is a simple combinatorial algorithm for the case where there is no cardinality constraint on the number of open centers. Our combinatorial algorithm is simpler and achieves better constant approximation factor compared to the LP approach.

Connected through hyperedge HE-1 (induced by source paper "faster balanced clusterings in high dimension"):
  with members:
    - "privacy preserving clustering with constraints"
    - "balanced k center clustering when k is a constant"
    - "approximation algorithms for clustering problems with lower bounds and outliers"
    - "local search yields a ptas for k means in doubling metrics"
    - "local search yields approximation schemes for k means and k median in euclidean and minor free metrics"
    - "distributed balanced partitioning via linear embedding"
    - "data driven resource allocation for distributed learning"
    - "faster algorithms for the constrained k means problem"

Connected through hyperedge HE-2 (induced by source paper "privacy preserving clustering with constraints"):
  with members:
    - "clustering with diversity"

Connected through hyperedge HE-3 (induced by source paper "balanced k center clustering when k is a constant"):
  with members:
    - "approximation algorithms for clustering problems with lower bounds and outliers"
    - "distributed balanced partitioning via linear embedding"
    - "data driven resource allocation for distributed learning"
    - "constant factor approximation for capacitated k center with outliers"
    - "fast clustering with lower bounds no customer too far no shop too small"
    - "lp rounding for k centers with non uniform hard capacities"

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)