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 "approximation algorithms for clustering problems with lower bounds and outliers"):
  Abstract: We consider clustering problems with {\em non-uniform lower bounds and outliers}, and obtain the {\em first approximation guarantees} for these problems. We have a set $\F$ of facilities with lower bounds $\{L_i\}_{i\in\F}$ and a set $\D$ of clients located in a common metric space $\{c(i,j)\}_{i,j\in\F\cup\D}$, and bounds $k$, $m$. A feasible solution is a pair $\bigl(S\sse\F,\sigma:\D\mapsto S\cup\{\mathsf{out}\}\bigr)$, where $\sigma$ specifies the client assignments, such that $|S|\leq k$, $|\sigma^{-1}(i)|\geq L_i$ for all $i\in S$, and $|\sigma^{-1}(\mathsf{out})|\leq m$. In the {\em lower-bounded min-sum-of-radii with outliers} (\lbksro) problem, the objective is to minimize $\sum_{i\in S}\max_{j\in\sigma^{-1}(i)}c(i,j)$, and in the {\em lower-bounded $k$-supplier with outliers} (\lbkso) problem, the objective is to minimize $\max_{i\in S}\max_{j\in\sigma^{-1}(i)}c(i,j)$. #R##N#We obtain an approximation factor of $12.365$ for \lbksro, which improves to $3.83$ for the non-outlier version (i.e., $m=0$). These also constitute the {\em first} approximation bounds for the min-sum-of-radii objective when we consider lower bounds and outliers {\em separately}. We apply the primal-dual method to the relaxation where we Lagrangify the $|S|\leq k$ constraint. The chief technical contribution and novelty of our algorithm is that, departing from the standard paradigm used for such constrained problems, we obtain an $O(1)$-approximation {\em despite the fact that we do not obtain a Lagrangian-multiplier-preserving algorithm for the Lagrangian relaxation}. We believe that our ideas have {broader applicability to other clustering problems with outliers as well.} #R##N#We obtain approximation factors of $5$ and $3$ respectively for \lbkso and its non-outlier version. These are the {\em first} approximation results for $k$-supplier with {\em non-uniform} lower bounds.

Includes members:
  Member "constant factor approximation for capacitated k center with outliers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "faster balanced clusterings in high dimension")
  Member "an improved approximation for k median and positive correlation in budgeted optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "individual fairness for k clustering")
  Member "fast clustering with lower bounds no customer too far no shop too small":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "constant factor approximation algorithms for parity constrained facility location problems")
  Member "approximating k median via pseudo approximation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fair correlation clustering")
  Member "improved approximation guarantees for lower bounded facility location":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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)