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: "dense scale selection over space time and space time"
  Abstract: Scale selection methods based on local extrema over scale of scale-normalized derivatives have been primarily developed to be applied sparsely---at image points where the magnitude of a scale-normalized differential expression additionally assumes local extrema over the domain where the data are defined. This paper presents a methodology for performing dense scale selection, so that hypotheses about local characteristic scales in images, temporal signals, and video can be computed at every image point and every time moment. A critical problem when designing mechanisms for dense scale selection is that the scale at which scale-normalized differential entities assume local extrema over scale can be strongly dependent on the local order of the locally dominant differential structure. To address this problem, we propose a methodology where local extrema over scale are detected of a quasi quadrature measure involving scale-space derivatives up to order two and propose two independent mechanisms to reduce the p...

Connected through hyperedge HE-1 (induced by source paper "provably scale covariant continuous hierarchical networks based on scale normalized differential expressions coupled in cascade"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "blurring the line between structure and learning to optimize and adapt receptive fields"
    - "provably scale covariant networks from oriented quasi quadrature measures in cascade"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "elastic improving cnns with dynamic scaling policies"
    - "from bow to cnn two decades of texture representation for texture classification"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "gabor convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "go with the flow perception refined physics simulation"):
  with members:
    - "seeing tree structure from vibration"
    - "repetition estimation"
    - "in defense of the triplet loss for person re identification"
    - "to fall or not to fall a visual approach to physical stability prediction"
    - "conditional similarity networks"
    - "what happens if learning to predict the effect of forces in images"
    - "newtonian image understanding unfolding the dynamics of objects in static images"
    - "deep feature learning with relative distance comparison for person re identification"

Connected through hyperedge HE-3 (induced by source paper "provably scale covariant networks from oriented quasi quadrature measures in cascade"):
  with members:
    - "a spatiotemporal oriented energy network for dynamic texture recognition"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "feature pyramid networks for object detection"
    - "spatial transformer networks"
    - "deep residual learning for image recognition"
    - "pcanet a simple deep learning baseline for image classification"
    - "very deep convolutional networks for large scale image recognition"
    - "invariant scattering convolution networks"

Connected through hyperedge HE-4 (induced by source paper "dynamic texture recognition using time causal and time recursive spatio temporal receptive fields"):
  with members:
    - "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"
    - "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)