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: "random warping series a random features method for time series embedding"
  Abstract: Time series data analytics has been a problem of substantial interests for decades, and Dynamic Time Warping (DTW) has been the most widely adopted technique to measure dissimilarity between time series. A number of global-alignment kernels have since been proposed in the spirit of DTW to extend its use to kernel-based estimation method such as support vector machine. However, those kernels suffer from diagonal dominance of the Gram matrix and a quadratic complexity w.r.t. the sample size. In this work, we study a family of alignment-aware positive definite (p.d.) kernels, with its feature embedding given by a distribution of \emph{Random Warping Series (RWS)}. The proposed kernel does not suffer from the issue of diagonal dominance while naturally enjoys a \emph{Random Features} (RF) approximation, which reduces the computational complexity of existing DTW-based techniques from quadratic to linear in terms of both the number and the length of time-series. We also study the convergence of the RF approximation for the domain of time series of unbounded length. Our extensive experiments on 16 benchmark datasets demonstrate that RWS outperforms or matches state-of-the-art classification and clustering methods in both accuracy and computational time. Our code and data is available at { \url{this https URL}}.

Connected through hyperedge HE-1 (induced by source paper "unsupervised scalable representation learning for multivariate time series"):
  with members:
    - "deep learning for time series classification a review"
    - "a theoretical analysis of contrastive unsupervised representation learning"
    - "the uea multivariate time series classification archive 2018"
    - "stochastic video generation with a learned prior"
    - "an efficient framework for learning sentence representations"
    - "som vae interpretable discrete representation learning on time series"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "representation learning with contrastive predictive coding"

Connected through hyperedge HE-2 (induced by source paper "scalable global alignment graph kernel using random features from node embedding to graph embedding"):
  with members:
    - "ddgk learning graph representations for deep divergence graph kernels"
    - "revisiting random binning features fast convergence and strong parallelizability"
    - "scalable spectral clustering using random binning features"
    - "sparse diffusion convolutional neural networks"
    - "dgcnn disordered graph convolutional neural network based on the gaussian mixture model"
    - "learning convolutional neural networks for graphs"
    - "primme_svds a high performance preconditioned svd solver for accurate large scale computations"
    - "on valid optimal assignment kernels and applications to graph classification"

Connected through hyperedge HE-3 (induced by source paper "multi stage deep classifier cascades for open world recognition"):
  with members:
    - "robust regression via online feature selection under adversarial data corruption"
    - "open world learning and application to product classification"
    - "learning deep features for one class classification"
    - "progressive neural networks"
    - "open world visual recognition using knowledge graphs"
    - "similarity preserving representation learning for time series clustering"
    - "icarl incremental classifier and representation learning"
    - "online open world recognition"

Connected through hyperedge HE-4 (induced by source paper "learning behavioral representations from wearable sensors"):
  with members:
    - "characterizing activity on the deep and dark web"
    - "discovering hidden structure in high dimensional human behavioral data via tensor factorization"
    - "tensor embedding a supervised framework for human behavioral data mining and prediction"
    - "non negative tensor factorization for human behavioral pattern mining in online games"
    - "toeplitz inverse covariance based clustering of multivariate time series data"
    - "modeling individual cyclic variation in human behavior"
    - "human gender classification a review"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-5 (induced by source paper "efficient global string kernel with random features beyond counting substructures"):
  with members:
    - "revisiting random binning features fast convergence and strong parallelizability"
    - "scalable spectral clustering using random binning features"
    - "word mover s embedding from word2vec to document embedding"
    - "lstm a search space odyssey"
    - "efficient approximation algorithms for string kernel based sequence classification"
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-6 (induced by source paper "efficient kernel based subsequence search for user identification from walking activity"):
  with members:
    - "on recursive edit distance kernels with application to time series classification"
    - "a kernel for time series based on global alignments"

Connected through hyperedge HE-7 (induced by source paper "scalable spectral clustering using random binning features"):
  with members:
    - "revisiting random binning features fast convergence and strong parallelizability"
    - "incremental eigenpair computation for graph laplacian matrices theory and applications"
    - "phase transitions and a model order selection criterion for spectral graph clustering"
    - "primme_svds a high performance preconditioned svd solver for accurate large scale computations"
    - "compressive spectral clustering"
    - "a preconditioned hybrid svd method for accurately computing singular triplets of large matrices"
    - "defining and evaluating network communities based on ground truth"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-8 (induced by source paper "space efficient feature maps for string alignment kernels"):
  with members:
    - "embedjoin efficient edit similarity joins via embeddings"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "orthogonal random features"
    - "linearized gmm kernels and normalized random fourier features"
    - "image based recommendations on styles and substitutes"
    - "fastfood approximate kernel expansions in loglinear time"
    - "a kernel for time series based on global alignments"

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.LG(Machine Learning)