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: "an improved approximation algorithm for the hard uniform capacitated k median problem"
  Abstract: In the $k$-median problem, given a set of locations, the goal is to select a subset of at most $k$ centers so as to minimize the total cost of connecting each location to its nearest center. We study the uniform hard capacitated version of the $k$-median problem, in which each selected center can only serve a limited number of locations. #R##N#Inspired by the algorithm of Charikar, Guha, Tardos and Shmoys, we give a $(6+10\alpha)$-approximation algorithm for this problem with increasing the capacities by a factor of $2+\frac{2}{\alpha}, \alpha\geq 4$, which improves the previous best $(32 l^2+28 l+7)$-approximation algorithm proposed by Byrka, Fleszar, Rybicki and Spoerhase violating the capacities by factor $2+\frac{3}{l-1}, l\in \{2,3,4,\dots\}$.

Connected through hyperedge HE-1 (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"
    - "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-2 (induced by source paper "constant factor approximation algorithms for uniform hard capacitated facility location problems natural lp is not too bad"):
  with members:
    - "constant approximation for capacitated k median with 1 epsilon capacity violation"
    - "an approximation algorithm for uniform capacitated k median problem with 1 epsilon capacity violation"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "constant approximation for capacitated k median with 1 epsilon capacity violation"):
  with members:
    - "an approximation algorithm for uniform capacitated k median problem with 1 epsilon capacity violation"
    - "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"
    - "bi factor approximation algorithms for hard capacitated k median problems"
    - "approximating k median via pseudo approximation"

Connected through hyperedge HE-4 (induced by source paper "data driven resource allocation for distributed learning"):
  with members:
    - "improved distributed principal component analysis"
    - "decompositions of triangle dense graphs"
    - "approximation algorithms for hard capacitated k facility location problems"
    - "bi factor approximation algorithms for hard capacitated k median problems"
    - "greedy facility location algorithms analyzed using dual fitting with factor revealing lp"

Connected through hyperedge HE-5 (induced by source paper "on uniform capacitated k median beyond the natural lp relaxation"):
  with members:
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "lp based algorithms for capacitated facility location"
    - "approximating capacitated k median with 1 epsilon k open facilities"
    - "approximation algorithms for hard capacitated k facility location problems"
    - "bi factor approximation algorithms for hard capacitated k median problems"
    - "approximating k median via pseudo approximation"
    - "an optimal bifactor approximation algorithm for the metric uncapacitated facility location problem"
    - "greedy facility location algorithms analyzed using dual fitting with factor revealing lp"

Connected through hyperedge HE-6 (induced by source paper "approximating capacitated k median with 1 epsilon k open facilities"):
  with members:
    - "on uniform capacitated k median beyond the natural lp relaxation"
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "an improved approximation algorithm for k median problem using a new factor revealing lp"
    - "approximation algorithms for hard capacitated k facility location problems"
    - "bi factor approximation algorithms for hard capacitated k median problems"
    - "approximating k median via pseudo approximation"
    - "an optimal bifactor approximation algorithm for the metric uncapacitated facility location problem"
    - "greedy facility location algorithms analyzed using dual fitting with factor revealing lp"

Connected through hyperedge HE-7 (induced by source paper "bi factor approximation algorithms for hard capacitated k median problems"):
  with members:
    - "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"
    - "approximating k median via pseudo approximation"
    - "lp rounding for k centers with non uniform hard capacities"
    - "greedy facility location algorithms analyzed using dual fitting with factor revealing lp"

Connected through hyperedge HE-8 (induced by source paper "approximation algorithms for hard capacitated k facility location problems"):
  with members:
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "bi factor approximation algorithms for hard capacitated k median problems"
    - "approximating k median via pseudo approximation"
    - "greedy facility location algorithms analyzed using dual fitting with factor revealing lp"

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)