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 clustering with lower bounds no customer too far no shop too small"
  Abstract: We study the \LowerBoundedCenter (\lbc) problem, which is a clustering problem that can be viewed as a variant of the \kCenter problem. In the \lbc problem, we are given a set of points P in a metric space and a lower bound \lambda, and the goal is to select a set C \subseteq P of centers and an assignment that maps each point in P to a center of C such that each center of C is assigned at least \lambda points. The price of an assignment is the maximum distance between a point and the center it is assigned to, and the goal is to find a set of centers and an assignment of minimum price. We give a constant factor approximation algorithm for the \lbc problem that runs in O(n \log n) time when the input points lie in the d-dimensional Euclidean space R^d, where d is a constant. We also prove that this problem cannot be approximated within a factor of 1.8-\epsilon unless P = \NP even if the input points are points in the Euclidean plane R^2.

Connected through hyperedge HE-1 (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-2 (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-3 (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"
    - "lp rounding for k centers with non uniform hard capacities"

Connected through hyperedge HE-4 (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"
    - "approximating k median via pseudo approximation"
    - "lp rounding for k centers with non uniform hard capacities"
    - "clustering with diversity"

Connected through hyperedge HE-5 (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"
    - "lp rounding for k centers with non uniform hard capacities"

Connected through hyperedge HE-6 (induced by source paper "approximation algorithms for clustering problems with lower bounds and outliers"):
  with members:
    - "constant factor approximation for capacitated k center with outliers"
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "approximating k median via pseudo approximation"
    - "improved approximation guarantees for lower bounded facility location"

Connected through hyperedge HE-7 (induced by source paper "net and prune a linear time algorithm for euclidean distance problems"):
  with members:
    - "coresets for k means and k median clustering and their applications"
    - "fast construction of nets in low dimensional metrics and their applications"

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.CG(Computational Geometry)