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: "nonsmooth analysis and subgradient methods for averaging in dynamic time warping spaces"
  Abstract: Time series averaging in dynamic time warping (DTW) spaces has been successfully applied to improve pattern recognition systems. This article proposes and analyzes subgradient methods for the problem of finding a sample mean in DTW spaces. The class of subgradient methods generalizes existing sample mean algorithms such as DTW Barycenter Averaging (DBA). We show that DBA is a majorize-minimize algorithm that converges to necessary conditions of optimality after finitely many iterations. Empirical results show that for increasing sample sizes the proposed stochastic subgradient (SSG) algorithm is more stable and finds better solutions in shorter time than the DBA algorithm on average. Therefore, SSG is useful in online settings and for non-small sample sizes. The theoretical and empirical results open new paths for devising sample mean algorithms: nonsmooth optimization methods and modified variants of pairwise averaging methods.

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"
    - "exact mean computation 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"
    - "exact mean computation 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:
    - "exact mean computation 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"
    - "exact mean computation 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"
    - "exact mean computation 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 "semi metrification of the dynamic time warping distance"):
  with members:
    - "on the existence of a sample mean 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-7 (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"
    - "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"
    - "on the existence of a sample mean 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"

Connected through hyperedge HE-8 (induced by source paper "asymmetric learning vector quantization for efficient nearest neighbor classification in dynamic time warping spaces"):
  with members:
    - "optimal warping paths are unique for almost every pair of time series"
    - "on the existence of a sample mean in dynamic time warping spaces"
    - "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"

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)