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: "constant approximation for capacitated k median with 1 epsilon capacity violation"
  Abstract: We study the Capacitated k-Median problem for which existing constant-factor approximation algorithms are all pseudo-approximations that violate either the capacities or the upper bound k on the number of open facilities. Using the natural LP relaxation for the problem, one can only hope to get the violation factor down to 2. Li [SODA'16] introduced a novel LP to go beyond the limit of 2 and gave a constant-factor approximation algorithm that opens $(1 + \epsilon )k$ facilities. #R##N#We use the configuration LP of Li [SODA'16] to give a constant-factor approximation for the Capacitated k-Median problem in a seemingly harder configuration: we violate only the capacities by $1 + \epsilon $. This result settles the problem as far as pseudo-approximation algorithms are concerned.

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 "improved local search based approximation algorithm for hard uniform capacitated k median problem"):
  with members:
    - "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"
    - "on uniform capacitated k median beyond the natural lp relaxation"
    - "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-4 (induced by source paper "the heterogeneous capacitated k center problem"):
  with members:
    - "the non uniform k center problem"
    - "on uniform capacitated k median beyond the natural lp relaxation"
    - "lp based algorithms for capacitated facility location"
    - "approximating capacitated k median with 1 epsilon k open facilities"
    - "lp rounding for k centers with non uniform hard capacities"
    - "quasi polynomial local search for restricted max min fair allocation"
    - "approximability of capacitated network design"
    - "a new approximation technique for resource allocation problems"

Connected through hyperedge HE-5 (induced by source paper "constant factor approximation algorithms for uniform hard capacitated facility location problems natural lp is not too bad"):
  with members:
    - "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"
    - "on uniform capacitated k median beyond the natural lp relaxation"
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "lp based algorithms for capacitated facility location"
    - "approximation algorithms for hard capacitated k facility location problems"
    - "improved approximation algorithms for matroid and knapsack median problems and applications"
    - "bi factor approximation algorithms for hard capacitated k median problems"

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)