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: "lp rounding for k centers with non uniform hard capacities"
  Abstract: In this paper we consider a generalization of the classical k-center problem with capacities. Our goal is to select k centers in a graph, and assign each node to a nearby center, so that we respect the capacity constraints on centers. The objective is to minimize the maximum distance a node has to travel to get to its assigned center. This problem is NP-hard, even when centers have no capacity restrictions and optimal factor 2 approximation algorithms are known. With capacities, when all centers have identical capacities, a 6 approximation is known with no better lower bounds than for the infinite capacity version. #R##N#While many generalizations and variations of this problem have been studied extensively, no progress was made on the capacitated version for a general capacity function. We develop the first constant factor approximation algorithm for this problem. Our algorithm uses an LP rounding approach to solve this problem, and works for the case of non-uniform hard capacities, when multiple copies of a node may not be chosen and can be extended to the case when there is a hard bound on the number of copies of a node that may be selected. In addition we establish a lower bound on the integrality gap of 7(5) for non-uniform (uniform) hard capacities. In addition we prove that if there is a (3-eps)-factor approximation for this problem then P=NP. #R##N#Finally, for non-uniform soft capacities we present a much simpler 11-approximation algorithm, which we find as one more evidence that hard capacities are much harder to deal with.

Connected through hyperedge HE-1 (induced by source paper "fair clustering with multiple colors"):
  with members:
    - "coresets for clustering with fairness constraints"
    - "fair regression quantitative definitions and reduction based algorithms"
    - "compositional fairness constraints for graph embeddings"
    - "scalable fair clustering"
    - "fair dimensionality reduction and iterative rounding for sdps"
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "privacy preserving clustering with constraints"
    - "constant factor fpt approximation for capacitated k median"

Connected through hyperedge HE-2 (induced by source paper "constant factor approximation algorithms for parity constrained facility location problems"):
  with members:
    - "a tree structure for dynamic facility location"
    - "on facility location with general lower bounds"
    - "online facility location with deletions"
    - "optimal parameterized algorithms for planar facility location problems using voronoi diagrams"
    - "facility location in evolving metrics"
    - "lp based algorithms for capacitated facility location"
    - "eight fifth approximation for the path tsp"
    - "local search based approximation algorithms for mobile facility location problems"

Connected through hyperedge HE-3 (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"
    - "capacitated center problems with two sided bounds and outliers"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "a unified framework for clustering constrained data without locality property"):
  with members:
    - "faster balanced clusterings in high dimension"
    - "faster algorithms for the constrained k means problem"
    - "the hardness of approximation of euclidean k means"
    - "net and prune a linear time algorithm for euclidean distance problems"
    - "parallel algorithms for geometric graph problems"
    - "a constant factor approximation algorithm for fault tolerant k median"
    - "fault tolerant clustering revisited"
    - "fast clustering with lower bounds no customer too far no shop too small"

Connected through hyperedge HE-5 (induced by source paper "the capacity constrained facility location problem"):
  with members:
    - "on voting and facility location"

Connected through hyperedge HE-6 (induced by source paper "capacitated covering problems in geometric spaces"):
  with members:
    - "replica placement on bounded treewidth graphs"
    - "iterative partial rounding for vertex cover with hard capacities"
    - "capacitated dominating set on planar graphs"
    - "tight algorithms for vertex cover with hard capacities on multigraphs and hypergraphs"
    - "on uniform capacitated k median beyond the natural lp relaxation"
    - "lp based algorithms for capacitated facility location"
    - "analytical approach to parallel repetition"
    - "improved approximation algorithms for geometric set cover"

Connected through hyperedge HE-7 (induced by source paper "capacitated center problems with two sided bounds and outliers"):
  with members:
    - "approximation algorithms for clustering problems with lower bounds and outliers"
    - "improved approximation algorithms for capacitated fault tolerant k center"
    - "data driven resource allocation for distributed learning"
    - "constant factor approximation for capacitated k center with outliers"
    - "matroid and knapsack center problems"
    - "fast clustering with lower bounds no customer too far no shop too small"
    - "approximating k median via pseudo approximation"
    - "clustering with diversity"

Connected through hyperedge HE-8 (induced by source paper "balanced k center clustering when k is a constant"):
  with members:
    - "capacitated center problems with two sided bounds and outliers"
    - "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"

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)