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: "blurring the line between structure and learning to optimize and adapt receptive fields"
  Abstract: The visual world is vast and varied, but its variations divide into structured and unstructured factors. We compose free-form filters and structured Gaussian filters, optimized end-to-end, to factorize deep representations and learn both local features and their degree of locality. Our semi-structured composition is strictly more expressive than free-form filtering, and changes in its structured parameters would require changes in free-form architecture. In effect this optimizes over receptive field size and shape, tuning locality to the data and task. Dynamic inference, in which the Gaussian structure varies with the input, adapts receptive field size to compensate for local scale variation. Optimizing receptive field size improves semantic segmentation accuracy on Cityscapes by 1-2 points for strong dilated and skip architectures and by up to 10 points for suboptimal designs. Adapting receptive fields by dynamic Gaussian structure further improves results, equaling the accuracy of free-form deformation while improving efficiency.

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"
    - "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"
    - "roto translation covariant convolutional networks for medical image analysis"

Connected through hyperedge HE-2 (induced by source paper "dynamic graph message passing networks"):
  with members:
    - "dynamic graph convolutional networks"
    - "pay less attention with lightweight and dynamic convolutions"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "ocnet object context network for scene parsing"
    - "learning a discriminative feature network for semantic segmentation"
    - "ccnet criss cross attention for semantic segmentation"
    - "relational inductive biases deep learning and graph networks"
    - "adaptive sampling towards fast graph representation learning"

Connected through hyperedge HE-3 (induced by source paper "deformable kernels adapting effective receptive fields for object deformation"):
  with members:
    - "deformable filter convolution for point cloud reasoning"
    - "making convolutional networks shift invariant again"
    - "learning by abstraction the neural state machine"
    - "condconv conditionally parameterized convolutions for efficient inference"
    - "dynamic scale inference by entropy minimization"
    - "kpconv flexible and deformable convolution for point clouds"
    - "selective kernel networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-4 (induced by source paper "dynamic scale inference by entropy minimization"):
  with members:
    - "semi supervised domain adaptation via minimax entropy"
    - "advent adversarial entropy minimization for domain adaptation in semantic segmentation"
    - "understanding the impact of entropy on policy optimization"
    - "on calibration of modern neural networks"
    - "deep value networks learn to evaluate and iteratively refine structured outputs"
    - "deep layer aggregation"
    - "deformable convolutional networks"
    - "film visual reasoning with a general conditioning layer"

Connected through hyperedge HE-5 (induced by source paper "neurvps neural vanishing point scanning via conic convolution"):
  with members:
    - "spherical cnns on unstructured grids"
    - "learning to reconstruct 3d manhattan wireframes from a single image"
    - "end to end wireframe parsing"
    - "texturenet consistent local parametrizations for learning from high resolution signals on meshes"
    - "active convolution learning the shape of convolution for image classification"
    - "deep learning for vanishing point detection using an inverse gnomonic projection"
    - "deformable convolutional networks"
    - "vpgnet vanishing point guided network for lane and road marking detection and recognition"

Connected through hyperedge HE-6 (induced by source paper "translation insensitive cnns"):
  with members:
    - "spatially adaptive filter units for compact and efficient deep neural networks"
    - "making convolutional networks shift invariant again"
    - "a systematic framework for natural perturbations from videos"
    - "rotdcf decomposition of convolutional filters for rotation equivariant deep networks"
    - "why do deep convolutional networks generalize so poorly to small image transformations"
    - "emergence of invariance and disentanglement in deep representations"
    - "structured receptive fields in cnns"
    - "inception v4 inception resnet and the impact of residual connections on learning"

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)