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: "alignment distances on systems of bags"
  Abstract: Recent research in image and video recognition indicates that many visual processes can be thought of as being generated by a time-varying generative model. A nearby descriptive model for visual processes is thus a statistical distribution that varies over time. Specifically, modeling visual processes as streams of histograms generated by a kernelized linear dynamic system turns out to be efficient. We refer to such a model as a system of bags. In this paper, we investigate systems of bags with special emphasis on dynamic scenes and dynamic textures. Parameters of linear dynamic systems suffer from ambiguities. In order to cope with these ambiguities in the kernelized setting, we develop a kernelized version of the alignment distance. For its computation, we use a Jacobi-type method and prove its convergence to a set of critical points. We employ it as a dissimilarity measure on Systems of Bags. As such, it outperforms other known dissimilarity measures for kernelized linear dynamic systems, in particular the Martin distance and the Maximum singular value distance, in every tested classification setting. A considerable margin can be observed in settings, where classification is performed with respect to an abstract mean of video sets. For this scenario, the presented approach can outperform the state-of-the-art techniques, such as dynamic fractal spectrum or orthogonal tensor dictionary learning.

Connected through hyperedge HE-1 (induced by source paper "dynamic variational autoencoders for visual process modeling"):
  with members:
    - "dyan a dynamical atoms based network for video prediction"
    - "two stream convolutional networks for dynamic texture synthesis"
    - "visual dynamics probabilistic future frame synthesis via cross convolutional networks"
    - "generating videos with scene dynamics"
    - "embed to control a locally linear latent dynamics model for control from raw images"
    - "deep predictive coding networks"

Connected through hyperedge HE-2 (induced by source paper "linearizing visual processes with convolutional variational autoencoders"):
  with members:
    - "dyan a dynamical atoms based network for video prediction"
    - "two stream convolutional networks for dynamic texture synthesis"
    - "dynamical textures modeling via joint video dictionary learning"
    - "visual dynamics probabilistic future frame synthesis via cross convolutional networks"
    - "group equivariant convolutional networks"
    - "generating videos with scene dynamics"
    - "procedural generation of videos to train deep action recognition networks"
    - "a mathematical theory of deep convolutional neural networks for feature extraction"

Connected through hyperedge HE-3 (induced by source paper "dynamic texture recognition using time causal and time recursive spatio temporal receptive fields"):
  with members:
    - "dense scale selection over space time and space time"
    - "convolutional neural network on three orthogonal planes for dynamic texture classification"
    - "time causal and time recursive spatio temporal receptive fields"
    - "dynamic texture and scene classification by transferring deep image features"
    - "spatiotemporal gabor filters a new method for dynamic texture recognition"

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)