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: "spatiotemporal gabor filters a new method for dynamic texture recognition"
  Abstract: This paper presents a new method for dynamic texture recognition based on spatiotemporal Gabor filters. Dynamic textures have emerged as a new field of investigation that extends the concept of self-similarity of texture image to the spatiotemporal domain. To model a dynamic texture, we convolve the sequence of images to a bank of spatiotemporal Gabor filters. For each response, a feature vector is built by calculating the energy statistic. As far as the authors know, this paper is the first to report an effective method for dynamic texture recognition using spatiotemporal Gabor filters. We evaluate the proposed method on two challenging databases and the experimental results indicate that the proposed method is a robust approach for dynamic texture recognition.

Connected through hyperedge HE-1 (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"
    - "alignment distances on systems of bags"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "dynamic texture analysis with diffusion in networks"):
  with members:
    - "convolutional neural network on three orthogonal planes for dynamic texture classification"
    - "a systematic comparison of supervised classifiers"

Connected through hyperedge HE-3 (induced by source paper "convolutional neural network on three orthogonal planes for dynamic texture classification"):
  with members:
    - "network dissection quantifying interpretability of deep visual representations"
    - "texture segmentation with fully convolutional networks"
    - "using filter banks in convolutional neural networks for texture classification"
    - "learning rotation invariant convolutional filters for texture classification"
    - "a theoretical analysis of deep neural networks for texture classification"
    - "dynamic texture and scene classification by transferring deep image features"
    - "understanding neural networks through deep visualization"
    - "deep filter banks for texture recognition description and segmentation"

Connected through hyperedge HE-4 (induced by source paper "understanding traffic density from large scale web camera data"):
  with members:
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "deep residual learning for image recognition"
    - "learning deconvolution network for semantic segmentation"
    - "ua detrac a new benchmark and protocol for multi object detection and tracking"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"
    - "fully convolutional networks for semantic segmentation"

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)