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: "a reduction theorem for the sample mean in dynamic time warping spaces"
  Abstract: Though the concept of sample mean in dynamic time warping (DTW) spaces is used in pattern recognition applications, its existence has neither been proved nor called into question. This article shows that a sample mean exists under general conditions that cover common variations of different DTW-spaces mentioned in the literature. The existence proofs are based on a Reduction Theorem that bounds the length of the candidate solutions we need to consider. The proposed results place the concept of sample mean in DTW-spaces on a sound mathematical foundation and serves as a first step towards a statistical theory of DTW-spaces.

Connected through hyperedge HE-1 (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"
    - "on the existence of a 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-2 (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"
    - "on the existence of a 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)