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 kernel for time series based on global alignments"
  Abstract: We propose in this paper a new family of kernels to handle time series, notably speech data, within the framework of kernel methods which includes popular algorithms such as the support vector machine. These kernels elaborate on the well known dynamic time warping (DTW) family of distances by considering the same set of elementary operations, namely substitutions and repetitions of tokens, to map a sequence onto another. Associating to each of these operations a given score, DTW algorithms use dynamic programming techniques to compute an optimal sequence of operations with high overall score, in this paper we consider instead the score spanned by all possible alignments, take a smoothed version of their maximum and derive a kernel out of this formulation. We prove that this kernel is positive definite under favorable conditions and show how it can be tuned effectively for practical applications as we report encouraging results on a speech recognition task.

Connected through hyperedge HE-1 (induced by source paper "fitting comparison and alignment of trajectories on positive semi definite matrices with application to action recognition"):
  with members:
    - "uav gesture a dataset for uav control and gesture recognition"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "human action recognition and prediction a survey"
    - "deep covariance descriptors for facial expression recognition"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "optimized skeleton based action recognition via sparsified graph regression"
    - "a novel geometric framework on gram matrix trajectories for human behavior understanding"
    - "automatic analysis of facial expressions based on deep covariance trajectories"

Connected through hyperedge HE-2 (induced by source paper "intelligent traffic monitoring systems for vehicle classification a survey"):
  with members:
    - "radio based traffic flow detection and vehicle classification for future smart cities"
    - "xgboost a scalable tree boosting system"
    - "car type recognition with deep neural networks"
    - "a large scale car dataset for fine grained categorization and verification"
    - "rethinking the inception architecture for computer vision"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "learning task grouping and overlap in multi task learning"

Connected through hyperedge HE-3 (induced by source paper "time series cluster kernels to exploit informative missingness and incomplete label information"):
  with members:
    - "learning representations for multivariate time series with missing data using temporal kernelized autoencoders"
    - "time series kernel similarities for predicting paroxysmal atrial fibrillation from ecgs"
    - "classification of postoperative surgical site infections from blood measurements with missing data using recurrent neural networks"
    - "directly modeling missing data in sequences with rnns improved classification of clinical time series"
    - "recurrent neural networks for multivariate time series with missing values"
    - "on recursive edit distance kernels with application to time series classification"

Connected through hyperedge HE-4 (induced by source paper "interpretable multiple kernel prototype learning for discriminative representation and feature selection"):
  with members:
    - "confident kernel sparse coding and dictionary learning"
    - "multiple kernel sparse representations for supervised and unsupervised learning"
    - "a review of learning vector quantization classifiers"

Connected through hyperedge HE-5 (induced by source paper "efficient kernel based subsequence search for user identification from walking activity"):
  with members:
    - "random warping series a random features method for time series embedding"
    - "on recursive edit distance kernels with application to time series classification"

Connected through hyperedge HE-6 (induced by source paper "times series averaging and denoising from a probabilistic perspective on time elastic kernels"):
  with members:
    - "on recursive edit distance kernels with application to time series classification"
    - "time warp edit distance with stiffness adjustment for time series matching"

Connected through hyperedge HE-7 (induced by source paper "on the separation of shape and temporal patterns in time series application to signature authentication"):
  with members:
    - "times series averaging and denoising from a probabilistic perspective on time elastic kernels"
    - "on recursive edit distance kernels with application to time series classification"

Connected through hyperedge HE-8 (induced by source paper "mlat metric learning for knn in streaming time series"):
  with members:
    - "toeplitz inverse covariance based clustering of multivariate time series data"

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)