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 for ordered k median"):
  Abstract: We study the Ordered k-Median problem, in which the solution is evaluated by first sorting the client connection costs and then multiplying them with a predefined non-increasing weight vector (higher connection costs are taken with larger weights). The problem unifies many fundamental clustering and location problems such as k-Median and k-Center. This generality, however, renders the problem intriguing from the algorithmic perspective. Recently, Aouad and Segev proposed a sophisticated local-search based O(log n) approximation algorithm for Ordered k-Median, extending the result by Tamir (2001) for the case of a rectangular weight vector, also known as k-Facility p-Centrum. The existence of a constant-factor approximation algorithm remained open, even for the special case with a rectangular weight vector. #R##N#Our main result is an LP-rounding constant-factor approximation algorithm for the (general) Ordered k-Median problem. #R##N#We first provide a new analysis of the rounding process by Charikar and Li (2012) for k-Median, when applied to a fractional solution obtained from solving an LP with a carefully modified objective function, results in an elegant 15-approximation for the rectangular case. In our analysis, the connection cost of a single client is partly charged to a deterministic budget related to a combinatorial bound based on guessing, and partly to a budget whose expected value is bounded with respect to the fractional LP-solution. Next we analyze objective-oblivious clustering that allows to handle multiple rectangles in the weight vector. Finally, with a more involved argument, we show that the clever distance bucketing by Aouad and Segev can be combined with the objective-oblivious version of our LP-rounding for the rectangular case, and that it results in a true, polynomial time, constant-factor approximation algorithm.

Includes members:
  Member "symmetric randomized dependent rounding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a lottery model for center type problems with outliers")
  Member "interpolating between k median and k center approximation algorithms for ordered k median":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "coresets for ordered weighted clustering")
  Member "better guarantees for k means and euclidean k median by primal dual algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fair correlation clustering")
  Member "an improved approximation for k median and positive correlation in budgeted optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "individual fairness for k clustering")
  Member "approximating k median via pseudo approximation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fair correlation clustering")
  Member "greedy facility location algorithms analyzed using dual fitting with factor revealing lp":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cache optimization models and algorithms")

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)