human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "exact mean computation in dynamic time warping spaces"
  Abstract: Dynamic time warping constitutes a major tool for analyzing time series. In particular, computing a mean series of a given sample of series in dynamic time warping spaces (by minimizing the Fr\'echet function) is a challenging computational problem, so far solved by several heuristic, inexact strategies. We spot several inaccuracies in the literature on exact mean computation in dynamic time warping spaces. Our contributions comprise an exact dynamic program computing a mean (useful for benchmarking and evaluating known heuristics). Empirical evaluations reveal significant deficits of the state-of-the-art heuristics in terms of their output quality. Finally, we give an exact polynomial-time algorithm for the special case of binary time series.

Connected through hyperedge HE-1 (induced by source paper "an average compress algorithm for the sample mean problem under dynamic time warping"):
  with members:
    - "hardness of consensus problems for circular strings and time series averaging"
    - "nonsmooth analysis and subgradient methods for averaging in dynamic time warping spaces"
    - "asymmetric learning vector quantization for efficient nearest neighbor classification in dynamic time warping spaces"
    - "on the existence of a sample mean in dynamic time warping spaces"
    - "adam a method for stochastic optimization"
    - "on the relation between the common labelling and the median graph"

Connected through hyperedge HE-2 (induced by source paper "making the dynamic time warping distance warping invariant"):
  with members:
    - "hardness of consensus problems for circular strings and time series averaging"
    - "the ucr time series archive"
    - "nonsmooth analysis and subgradient methods for averaging in dynamic time warping spaces"
    - "asymmetric learning vector quantization for efficient nearest neighbor classification in dynamic time warping spaces"
    - "on the existence of a sample mean in dynamic time warping spaces"
    - "faster retrieval with a two pass dynamic time warping lower bound"

Connected through hyperedge HE-3 (induced by source paper "trainable time warping aligning time series in the continuous time domain"):
  with members:
    - "nonsmooth analysis and subgradient methods for averaging in dynamic time warping spaces"
    - "asymmetric learning vector quantization for efficient nearest neighbor classification in dynamic time warping spaces"
    - "optimal warping paths are unique for almost every pair of time series"
    - "adam a method for stochastic optimization"
    - "voice recognition algorithms using mel frequency cepstral coefficient mfcc and dynamic time warping dtw techniques"

Connected through hyperedge HE-4 (induced by source paper "hardness of consensus problems for circular strings and time series averaging"):
  with members:
    - "on the hardness of computing an average curve"
    - "nonsmooth analysis and subgradient methods for averaging in dynamic time warping spaces"
    - "dynamic time warping and geometric edit distance breaking the quadratic barrier"
    - "quadratic conditional lower bounds for string problems and dynamic time warping"
    - "on the closest string and substring problems"

Connected through hyperedge HE-5 (induced by source paper "tight hardness results for consensus problems on circular strings and time series"):
  with members:
    - "on the hardness of computing an average curve"
    - "nonsmooth analysis and subgradient methods for averaging in dynamic time warping spaces"
    - "dynamic time warping and geometric edit distance breaking the quadratic barrier"
    - "quadratic conditional lower bounds for string problems and dynamic time warping"
    - "on the closest string and substring problems"

Connected through hyperedge HE-6 (induced by source paper "optimal warping paths are unique for almost every pair of time series"):
  with members:
    - "nonsmooth analysis and subgradient methods for averaging in dynamic time warping spaces"
    - "asymmetric learning vector quantization for efficient nearest neighbor classification in dynamic time warping spaces"
    - "warped linear models for time series classification"

Connected through hyperedge HE-7 (induced by source paper "on the existence of a sample mean in dynamic time warping spaces"):
  with members:
    - "nonsmooth analysis and subgradient methods for averaging in dynamic time warping spaces"

Question: Please tell me which class the center node 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)