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: "on uniform capacitated k median beyond the natural lp relaxation"
  Abstract: In this paper, we study the uniform capacitated $k$-median problem. Obtaining a constant approximation algorithm for this problem is a notorious open problem; most previous works gave constant approximations by either violating the capacity constraints or the cardinality constraint. Notably, all these algorithms are based on the natural LP-relaxation for the problem. The LP-relaxation has unbounded integrality gap, even when we are allowed to violate the capacity constraints or the cardinality constraint by a factor of $2-\epsilon$. #R##N#Our result is an $\exp(O(1/\epsilon^2))$-approximation algorithm for the problem that violates the cardinality constraint by a factor of $1+\epsilon$. This is already beyond the capability of the natural LP relaxation, as it has unbounded integrality gap even if we are allowed to open $(2-\epsilon)k$ facilities. Indeed, our result is based on a novel LP for this problem. #R##N#The version as we described is the hard-capacitated version of the problem, as we can only open one facility at each location. This is as opposed to the soft-capacitated version, in which we are allowed to open more than one facilities at each location. We give a simple proof that in the uniform capacitated case, the soft-capacitated version and the hard-capacitated version are actually equivalent, up to a small constant loss in the approximation ratio.

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 "streaming balanced clustering"):
  with members:
    - "a constant parameterized approximation for hard capacitated k means"
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "constant factor fpt approximation for capacitated k median"
    - "nearly optimal dynamic k means clustering for high dimensional data"
    - "relative error tensor low rank approximation"
    - "clustering high dimensional dynamic data streams"
    - "distributed low rank approximation of implicit functions of a matrix"
    - "new frameworks for offline and streaming coreset constructions"

Connected through hyperedge HE-3 (induced by source paper "a constant fpt approximation algorithm for hard capacitated k means"):
  with members:
    - "constant factor fpt approximation for capacitated k median"
    - "greedy bi criteria approximations for k medians and k means"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "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"
    - "on variants of k means clustering"
    - "an approximation algorithm for uniform capacitated k median problem with 1 epsilon capacity violation"
    - "improved and simplified inapproximability for k means"

Connected through hyperedge HE-4 (induced by source paper "a constant parameterized approximation for hard capacitated k means"):
  with members:
    - "constant factor fpt approximation for capacitated k median"
    - "greedy bi criteria approximations for k medians and k means"
    - "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"
    - "an approximation algorithm for uniform capacitated k median problem with 1 epsilon capacity violation"
    - "the hardness of approximation of euclidean k means"

Connected through hyperedge HE-5 (induced by source paper "joint switch upgrade and controller deployment in hybrid software defined networks"):
  with members:
    - "morph an adaptive framework for efficient and byzantine fault tolerant sdn control plane"
    - "divide and conquer partitioning ospf networks with sdn"
    - "software defined networking a comprehensive survey"

Connected through hyperedge HE-6 (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-7 (induced by source paper "improved local search based approximation algorithm for hard uniform capacitated k median problem"):
  with members:
    - "constant approximation for capacitated k median with 1 epsilon capacity violation"
    - "constant factor approximation algorithms for uniform hard capacitated facility location problems natural lp is not too bad"
    - "an approximation algorithm for uniform capacitated k median problem with 1 epsilon capacity violation"
    - "an improved approximation algorithm for the hard uniform capacitated k median problem"
    - "approximation algorithms for capacitated facility location problem with penalties"
    - "approximation algorithms for hard capacitated k facility location problems"
    - "bi factor approximation algorithms for hard capacitated k median problems"

Connected through hyperedge HE-8 (induced by source paper "on the cost of essentially fair clusterings"):
  with members:
    - "fair clustering through fairlets"
    - "constant approximation for k median and k means with outliers via iterative rounding"
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "approximation algorithms for hard capacitated k facility location problems"
    - "fairness through awareness"
    - "clustering with diversity"

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)