human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "constant factor approximation algorithms for uniform hard capacitated facility location problems natural lp is not too bad"):
  Abstract: In this paper, we give first constant factor approximation for capacitated knapsack median problem (CKM) for hard uniform capacities, violating the budget only by an additive factor of $f_{max}$ where $f_{max}$ is the maximum cost of a facility opened by the optimal and violating capacities by $(2+\epsilon)$ factor. Natural LP for the problem is known to have an unbounded integrality gap when any one of the two constraints is allowed to be violated by a factor less than $2$. Thus, we present a result which is very close to the best achievable from the natural LP. To the best of our knowledge, the problem has not been studied earlier. #R##N#For capacitated facility location problem with uniform capacities, a constant factor approximation algorithm is presented violating the capacities a little ($1 + \epsilon$). Though constant factor results are known for the problem without violating the capacities, the result is interesting as it is obtained by rounding the solution to the natural LP, which is known to have an unbounded integrality gap without violating the capacities. Thus, we achieve the best possible from the natural LP for the problem. The result shows that natural LP is not too bad. #R##N#Finally, we raise some issues with the proofs of the results presented in~\cite{capkmByrkaFRS2013} for capacitated $k$-facility location problem (C$k$FLP).~\cite{capkmByrkaFRS2013} presents $O(1/\epsilon^2)$ approximation violating the capacities by a factor of $(2 + \epsilon)$ using dependent rounding. We first fix these issues using our techniques. Also, it can be argued that (deterministic) pipage rounding cannot be used to open the facilities instead of dependent rounding. Our techniques for CKM provide a constant factor approximation for CkFLP violating the capacities by $(2 + \epsilon)$.

Includes members:
  Member "constant approximation for capacitated k median with 1 epsilon capacity violation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fair clustering with multiple colors")
  Member "an approximation algorithm for uniform capacitated k median problem with 1 epsilon capacity violation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "streaming balanced clustering")
  Member "an improved approximation algorithm for the hard uniform capacitated k median problem":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "improved local search based approximation algorithm for hard uniform capacitated k median problem")
  Member "on uniform capacitated k median beyond the natural lp relaxation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fair clustering with multiple colors")
  Member "an improved approximation for k median and positive correlation in budgeted optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "individual fairness for k clustering")
  Member "lp based algorithms for capacitated facility location":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "easy capacitated facility location problems with connections to lot sizing")
  Member "approximation algorithms for hard capacitated k facility location problems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "facility location problem with capacity constraints algorithmic and mechanism design perspectives")
  Member "improved approximation algorithms for matroid and knapsack median problems and applications":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "fair algorithms for clustering")

Question: Please tell me which class the source paper 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)