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 recursive edit distance kernels with application to time series classification"
  Abstract: This paper proposes some extensions to the work on kernels dedicated to string or time series global alignment based on the aggregation of scores obtained by local alignments. The extensions that we propose allow us to construct, from classical recursive definition of elastic distances, recursive edit distance (or time-warp) kernels that are positive definite if some sufficient conditions are satisfied. The sufficient conditions we end up with are original and weaker than those proposed in earlier works, although a recursive regularizing term is required to get proof of the positive definiteness as a direct consequence of the Haussler’s convolution theorem. Furthermore, the positive definiteness is maintained when a symmetric corridor is used to reduce the search space, and thus the algorithmic complexity, which is quadratic in the worst case. The classification experiment we conducted on three classical time-warp distances (two of which are metrics), using support vector machine classifier, leads to the conclusion that when the pairwise distance matrix obtained from the training data is far from definiteness, the positive definite recursive elastic kernels outperform in general the distance substituting kernels for several classical elastic distances we have tested.

Connected through hyperedge HE-1 (induced by source paper "multi view time series classification via global local correlative channel aware fusion mechanism"):
  with members:
    - "multivariate lstm fcns for time series classification"
    - "ev action electromyography vision multi modal action dataset"
    - "reservoir computing approaches for representation and classification of multivariate time series"
    - "deep multi view spatial temporal network for taxi demand prediction"
    - "memory fusion network for multi view sequential learning"
    - "multi scale convolutional neural networks for time series classification"
    - "temporal segment networks towards good practices for deep action recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-2 (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"
    - "a kernel for time series based on global alignments"

Connected through hyperedge HE-3 (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"
    - "a kernel for time series based on global alignments"

Connected through hyperedge HE-4 (induced by source paper "times series averaging and denoising from a probabilistic perspective on time elastic kernels"):
  with members:
    - "time warp edit distance with stiffness adjustment for time series matching"
    - "a kernel for time series based on global alignments"

Connected through hyperedge HE-5 (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"
    - "a kernel for time series based on global alignments"

Connected through hyperedge HE-6 (induced by source paper "sparsification of the alignment path search space in dynamic time warping"):
  with members:
    - "times series averaging and denoising from a probabilistic perspective on time elastic kernels"
    - "an empirical evaluation of similarity measures for time series classification"
    - "sparsedtw a novel approach to speed up dynamic time warping"
    - "time warp edit distance with stiffness adjustment for time series matching"
    - "a kernel for time series based on global alignments"

Connected through hyperedge HE-7 (induced by source paper "adaptive down sampling and dimension reduction in time elastic kernel machines for efficient recognition of isolated gestures"):
  with members:
    - "toward a motor theory of sign language perception"
    - "speeding up simplification of polygonal curves using nested approximations"
    - "time warp edit distance with stiffness adjustment for time series matching"
    - "a kernel for time series based on global alignments"

Connected through hyperedge HE-8 (induced by source paper "tracking idea flows between social groups"):
  with members:
    - "from micro to macro uncovering and predicting information cascading process with behavioral dynamics"
    - "bayesian conditional cointegration"
    - "information diffusion and external influence in networks"
    - "inferring networks of diffusion and influence"

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)