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 "proportional approval voting harmonic k median and negative association"):
  Abstract: We study Proportional Approval Voting (PAV)---an election system used for choosing representatives bodies, such as parliaments, based on preferences of a population of voters over individual candidates. We observe that the problem of computing the outcome of PAV can be cast as a variant of the standard k-median problem. Our main result is that, due to the specific (harmonic) cost structure, the problem allows constant factor approximation that does not require the underlying connection costs to be metric. To the best of our knowledge this is the first constant factor approximation algorithm for a facility location type problem not assuming triangle inequalities for connection costs. #R##N#The algorithm we propose is a standard dependent rounding routine [Srinivasan FOCS'01] applied to the solution of a natural LP-relaxation of the problem. The rounding process is well known to produce distributions over integral solutions satisfying Negative Correlation (NC). This, however, is not sufficient for our analysis. We show that, if the rounding process follows a predefined tournament tree, dependent rounding has a stronger property that we call Binary Negative Association (BNA). We also show that if the pairs of elements to be correlated are being selected by an adaptive adversary, then the BNA may not hold, while the proof of NC does not depend on the order of selection of elements. We believe the BNA property of dependent rounding is of independent interest and that it may find applications in other settings.

Includes members:
  Member "approximation and parameterized complexity of minimax approval voting":
    also connected through other hyperedges:
      (none)
  Member "finding a collective set of items from proportional multirepresentation to group recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "evaluating approval based multiwinner voting in terms of robustness to noise")
  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 "a constant factor approximation algorithm for fault tolerant k median":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a unified framework for clustering constrained data without locality property")
  Member "analytical approach to parallel repetition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an improved algorithm for dynamic set cover")
  Member "multimode control attacks on elections":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the complexity of constructive control under nearly single peaked preferences")

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)