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: "a spatiotemporal oriented energy network for dynamic texture recognition"
  Abstract: This paper presents a novel hierarchical spatiotemporal orientation representation for spacetime image analysis. It is designed to combine the benefits of the multilayer architecture of ConvNets and a more controlled approach to spacetime analysis. A distinguishing aspect of the approach is that unlike most contemporary convolutional networks no learning is involved; rather, all design decisions are specified analytically with theoretical motivations. This approach makes it possible to understand what information is being extracted at each stage and layer of processing as well as to minimize heuristic choices in design. Another key aspect of the network is its recurrent nature, whereby the output of each layer of processing feeds back to the input. To keep the network size manageable across layers, a novel cross-channel feature pooling is proposed. The multilayer architecture that results systematically reveals hierarchical image structure in terms of multiscale, multiorientation properties of visual spacetime. To illustrate its utility, the network has been applied to the task of dynamic texture recognition. Empirical evaluation on multiple standard datasets shows that it sets a new state-of-the-art.

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 "provably scale covariant hierarchical continuous networks based on scale normalized differential expressions coupled in cascade"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "provably scale covariant networks from oriented quasi quadrature measures in cascade"
    - "estimating information flow in neural networks"
    - "mathematics of deep learning"
    - "dynamic routing between capsules"
    - "synthesizing robust adversarial examples"
    - "a boundary tilting persepective on the phenomenon of adversarial examples"
    - "a unified multi scale deep convolutional neural network for fast object detection"

Connected through hyperedge HE-3 (induced by source paper "provably scale covariant networks from oriented quasi quadrature measures in cascade"):
  with members:
    - "dense scale selection over space time and space time"
    - "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 "what do we understand about convolutional networks"):
  with members:
    - "network dissection quantifying interpretability of deep visual representations"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "oriented response networks"
    - "active convolution learning the shape of convolution for image classification"
    - "deformable convolutional networks"
    - "learning texture manifolds with the periodic spatial gan"
    - "learning to learn by gradient descent by gradient descent"
    - "harmonic networks deep translation and rotation equivariance"

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)