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: "on data driven saak transform"
  Abstract: Being motivated by the multilayer RECOS (REctified-COrrelations on a Sphere) transform, we develop a data-driven Saak (Subspace approximation with augmented kernels) transform in this work. The Saak transform consists of three steps: 1) building the optimal linear subspace approximation with orthonormal bases using the second-order statistics of input vectors, 2) augmenting each transform kernel with its negative, 3) applying the rectified linear unit (ReLU) to the transform output. The Karhunen-Lo\'eve transform (KLT) is used in the first step. The integration of Steps 2 and 3 is powerful since they resolve the sign confusion problem, remove the rectification loss and allow a straightforward implementation of the inverse Saak transform at the same time. Multiple Saak transforms are cascaded to transform images of a larger size. All Saak transform kernels are derived from the second-order statistics of input random vectors in a one-pass feedforward manner. Neither data labels nor backpropagation is used in kernel determination. Multi-stage Saak transforms offer a family of joint spatial-spectral representations between two extremes; namely, the full spatial-domain representation and the full spectral-domain representation. We select Saak coefficients of higher discriminant power to form a feature vector for pattern recognition, and use the MNIST dataset classification problem as an illustrative example.

Connected through hyperedge HE-1 (induced by source paper "pointhop a lightweight learning model on point sets for 3d classification"):
  with members:
    - "pointhop an explainable machine learning method for point cloud classification"
    - "interpretable convolutional neural networks via feedforward design"
    - "meshnet mesh neural network for 3d shape representation"
    - "dynamic graph cnn for learning on point clouds"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "a saak transform approach to efficient scalable and robust handwritten digits recognition"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "foldingnet point cloud auto encoder via deep grid deformation"

Connected through hyperedge HE-2 (induced by source paper "pointhop an explainable machine learning method for point cloud classification"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "ensembles of feedforward designed convolutional neural networks"
    - "interpretable convolutional neural networks via feedforward design"
    - "pointpillars fast encoders for object detection from point clouds"
    - "meshnet mesh neural network for 3d shape representation"
    - "dynamic graph cnn for learning on point clouds"
    - "pvnet a joint convolutional network of point cloud and multi view for 3d shape recognition"
    - "pointnetvlad deep point cloud based retrieval for large scale place recognition"

Connected through hyperedge HE-3 (induced by source paper "robustness of saak transform against adversarial attacks"):
  with members:
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "feature denoising for improving adversarial robustness"
    - "universal adversarial training"
    - "deflecting adversarial attacks with pixel deflection"
    - "shield fast practical defense and vaccination for deep learning using jpeg compression"
    - "sok security and privacy in machine learning"
    - "defense against adversarial attacks with saak transform"
    - "feature squeezing detecting adversarial examples in deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "visualization discriminability and applications of interpretable saak features"):
  with members:
    - "defense against adversarial attacks with saak transform"
    - "a saak transform approach to efficient scalable and robust handwritten digits recognition"
    - "safetynet detecting and rejecting adversarial examples robustly"
    - "on the statistical detection of adversarial examples"
    - "adversarial examples attacks and defenses for deep learning"
    - "understanding convolutional neural networks with a mathematical model"
    - "adversarial examples in the physical world"
    - "deepfool a simple and accurate method to fool deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "semi supervised learning via feedforward designed convolutional neural networks"):
  with members:
    - "ensembles of feedforward designed convolutional neural networks"
    - "interpretable convolutional neural networks via feedforward design"
    - "a saak transform approach to efficient scalable and robust handwritten digits recognition"
    - "good semi supervised learning that requires a bad gan"
    - "interpretable convolutional neural networks"
    - "improved techniques for training gans"
    - "understanding convolutional neural networks with a mathematical model"

Connected through hyperedge HE-6 (induced by source paper "urbanrhythm revealing urban dynamics hidden in mobility data"):
  with members:
    - "city of the people for the people sensing urban dynamics via social media interactions"
    - "urban pulse capturing the rhythm of cities"
    - "a saak transform approach to efficient scalable and robust handwritten digits recognition"
    - "joint unsupervised learning of deep representations and image clusters"
    - "automatic city region analysis for urban routing"

Connected through hyperedge HE-7 (induced by source paper "ensembles of feedforward designed convolutional neural networks"):
  with members:
    - "interpretable convolutional neural networks via feedforward design"
    - "a saak transform approach to efficient scalable and robust handwritten digits recognition"
    - "interpretable convolutional neural networks"
    - "understanding convolutional neural networks with a mathematical model"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "pixelhop a successive subspace learning ssl method for object classification"):
  with members:
    - "pointhop an explainable machine learning method for point cloud classification"
    - "interpretable convolutional neural networks via feedforward design"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "understanding convolutional neural networks with a mathematical model"
    - "pcanet a simple deep learning baseline for image classification"

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)