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: "on the existence of a sample mean in dynamic time warping spaces"
  Abstract: The concept of sample mean in dynamic time warping (DTW) spaces has been successfully applied to improve pattern recognition systems and generalize centroid-based clustering algorithms. Its existence has neither been proved nor challenged. This article presents sufficient conditions for existence of a sample mean in DTW spaces. The proposed result justifies prior work on approximate mean algorithms, sets the stage for constructing exact mean algorithms, and is a first step towards a statistical theory of DTW spaces.

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"
    - "exact mean computation in dynamic time warping spaces"
    - "asymmetric learning vector quantization for efficient nearest neighbor classification 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"
    - "exact mean computation in dynamic time warping spaces"
    - "asymmetric learning vector quantization for efficient nearest neighbor classification 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 "semi metrification of the dynamic time warping distance"):
  with members:
    - "nonsmooth analysis and subgradient methods for averaging in dynamic time warping spaces"
    - "faster retrieval with a two pass dynamic time warping lower bound"
    - "time warp edit distance with stiffness adjustment for time series matching"

Connected through hyperedge HE-4 (induced by source paper "exact mean computation in dynamic time warping spaces"):
  with members:
    - "times series averaging and denoising from a probabilistic perspective on time elastic kernels"
    - "tight hardness results for consensus problems on circular strings and time series"
    - "nonsmooth analysis and subgradient methods for averaging in dynamic time warping spaces"
    - "optimal warping paths are unique for almost every pair of time series"
    - "fuzzy c shape a new algorithm for clustering finite time series waveforms"
    - "shapedtw shape dynamic time warping"
    - "dynamic time warping and geometric edit distance breaking the quadratic barrier"
    - "quadratic conditional lower bounds for string problems and dynamic time warping"

Connected through hyperedge HE-5 (induced by source paper "asymmetric learning vector quantization for efficient nearest neighbor classification in dynamic time warping spaces"):
  with members:
    - "nonsmooth analysis and subgradient methods for averaging in dynamic time warping spaces"
    - "optimal warping paths are unique for almost every pair of time series"
    - "a reduction theorem for the sample mean in dynamic time warping spaces"
    - "a review of learning vector quantization classifiers"
    - "voice recognition algorithms using mel frequency cepstral coefficient mfcc and dynamic time warping dtw techniques"

Connected through hyperedge HE-6 (induced by source paper "nonsmooth analysis and subgradient methods for averaging in dynamic time warping spaces"):
  with members:
    - "asymmetric learning vector quantization for efficient nearest neighbor classification in dynamic time warping spaces"
    - "a reduction theorem for the sample mean in dynamic time warping spaces"
    - "practical recommendations for gradient based training of deep architectures"

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.CV(Computer Vision and Pattern Recognition)