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 "strongly polynomial efficient approximation scheme for segmentation"):
  Abstract: Abstract   Partitioning a sequence of length  n  into  k  coherent segments ( Seg ) is one of the classic optimization problems. As long as the optimization criterion is additive,  Seg  can be solved exactly in    O   (    n    2    k  )     time using a classic dynamic program. Due to the quadratic term, computing the exact segmentation may be too expensive for long sequences, which has led to development of approximate solutions. We consider an existing estimation scheme that computes    (  1  +  ϵ  )    approximation in polylogarithmic time. We augment this algorithm, making it strongly polynomial. We do this by first solving a slightly different segmentation problem ( MaxSeg ), where the quality of the segmentation is the maximum penalty of an individual segment. By using this solution to initialize the estimation scheme, we are able to obtain a strongly polynomial algorithm. In addition, we consider a cumulative version of  Seg , where we are asked to discover the optimal segmentation for each prefix of the input sequence. We propose a strongly polynomial algorithm that yields    (  1  +  ϵ  )    approximation in    O   (  n    k    2    /  ϵ  )     time. Finally, we consider a cumulative version of  MaxSeg , and show that we can solve the problem in    O   (  n  k  log  ⁡  k  )     time.

Includes members:
  Member "fast sequence segmentation using log linear models":
    also connected through other hyperedges:
      (none)
  Member "approximate queries and representations for large data sequences":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "shapesearch a flexible and efficient system for shape based exploration of trendlines")

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)