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 "an average compress algorithm for the sample mean problem under dynamic time warping"):
  Abstract: Computing a sample mean of time series under dynamic time warping (DTW) is NP-hard. Consequently, there is an ongoing research effort to devise efficient heuristics. The majority of heuristics have been developed for the constrained sample mean problem that assumes a solution of predefined length. In contrast, research on the unconstrained sample mean problem is underdeveloped. In this article, we propose a generic average-compress (AC) algorithm for solving the unconstrained problem. The algorithm alternates between averaging (A-step) and compression (C-step). The A-step takes an initial guess as input and returns an approximation of a sample mean. Then the C-step reduces the length of the approximate solution. The compressed approximation serves as initial guess of the A-step in the next iteration. The purpose of the C-step is to direct the algorithm to more promising solutions of shorter length. The proposed algorithm is generic in the sense that any averaging and any compression method can be used. Experimental results show that the AC algorithm substantially outperforms current state-of-the-art algorithms for time series averaging.

Includes members:
  Member "hardness of consensus problems for circular strings and time series averaging":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "making the dynamic time warping distance warping invariant")
  Member "nonsmooth analysis and subgradient methods for averaging in dynamic time warping spaces":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "making the dynamic time warping distance warping invariant")
  Member "exact mean computation in dynamic time warping spaces":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "making the dynamic time warping distance warping invariant")
  Member "asymmetric learning vector quantization for efficient nearest neighbor classification in dynamic time warping spaces":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "insights into lstm fully convolutional networks for time series classification")
  Member "on the existence of a sample mean in dynamic time warping spaces":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "making the dynamic time warping distance warping invariant")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "on the relation between the common labelling and the median graph":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "properties of the sample mean in graph spaces and the majorize minimize mean algorithm")

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)