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: "generalized backpropagation etude de cas orthogonality"
  Abstract: This paper introduces an extension of the backpropagation algorithm that enables us to have layers with constrained weights in a deep network. In particular, we make use of the Riemannian geometry and optimization techniques on matrix manifolds to step outside of normal practice in training deep networks, equipping the network with structures such as orthogonality or positive definiteness. Based on our development, we make another contribution by introducing the Stiefel layer, a layer with orthogonal weights. Among various applications, Stiefel layers can be used to design orthogonal filter banks, perform dimensionality reduction and feature extraction. We demonstrate the benefits of having orthogonality in deep networks through a broad set of experiments, ranging from unsupervised feature learning to fine-grained image classification.

Connected through hyperedge HE-1 (induced by source paper "concept whitening for interpretable image recognition"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "the convolutional tsetlin machine"
    - "educe explaining model decisions through unsupervised concepts extraction"
    - "saccader improving accuracy of hard attention models for vision"
    - "cheap orthogonal constraints in neural networks a simple parametrization of the orthogonal and unitary group"
    - "on concept based explanations in deep neural networks"
    - "iterative normalization beyond standardization towards efficient whitening"
    - "this looks like that deep learning for interpretable image recognition"

Connected through hyperedge HE-2 (induced by source paper "self orthogonality module a network architecture plug in for learning orthogonal filters"):
  with members:
    - "orthogonal deep neural networks"
    - "unsupervised deep feature transfer for low resolution image classification"
    - "adversarially approximated autoencoder for image generation and manipulation"
    - "mdfn multi scale deep feature learning network for object detection"
    - "towards learning affine invariant representations via data efficient cnns"
    - "boosting occluded image classification via subspace decomposition based estimation of deep features"
    - "interpretable convolutional neural networks via feedforward design"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"

Connected through hyperedge HE-3 (induced by source paper "abd net attentive but diverse person re identification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deep representation learning with part loss for person re identification"
    - "feature mask network for person re identification"
    - "attribute aware attention model for fine grained representation learning"
    - "multi level factorisation net for person re identification"
    - "end to end deep kronecker product matching for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "triplet based deep similarity learning for person re identification"

Connected through hyperedge HE-4 (induced by source paper "orthogonal convolutional neural networks"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "autogan neural architecture search for generative adversarial networks"
    - "cheap orthogonal constraints in neural networks a simple parametrization of the orthogonal and unitary group"
    - "simple black box adversarial attacks"
    - "superposition of many models into one"
    - "the singular values of convolutional layers"
    - "spectral normalization for generative adversarial networks"
    - "can we gain more from orthogonality regularizations in training deep cnns"

Connected through hyperedge HE-5 (induced by source paper "orthogonal wasserstein gans"):
  with members:
    - "on gans and gmms"
    - "sorting out lipschitz function approximation"
    - "spectral normalization for generative adversarial networks"
    - "can we gain more from orthogonality regularizations in training deep cnns"
    - "banach wasserstein gan"
    - "large scale gan training for high fidelity natural image synthesis"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-6 (induced by source paper "walking on the edge fast low distortion adversarial examples"):
  with members:
    - "targeted mismatch adversarial attack query with a flower to retrieve the tower"
    - "adversarial attacks and defences competition"
    - "on the suitability of l_p norms for creating and preventing adversarial examples"
    - "universal perturbation attack against image retrieval"
    - "decoupling direction and norm for efficient gradient based l2 adversarial attacks and defenses"
    - "audio adversarial examples targeted attacks on speech to text"
    - "why do deep convolutional networks generalize so poorly to small image transformations"
    - "adversarial examples for semantic segmentation and object detection"

Connected through hyperedge HE-7 (induced by source paper "orthogonal relation transforms with graph context modeling for knowledge graph embedding"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "learning attention based embeddings for relation prediction in knowledge graphs"
    - "tucker tensor factorization for knowledge graph completion"
    - "fast graph representation learning with pytorch geometric"
    - "a capsule network based embedding model for knowledge graph completion and search personalization"
    - "can we gain more from orthogonality regularizations in training deep cnns"
    - "end to end structure aware convolutional networks for knowledge base completion"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"

Connected through hyperedge HE-8 (induced by source paper "cheap orthogonal constraints in neural networks a simple parametrization of the orthogonal and unitary group"):
  with members:
    - "riemannian stochastic variance reduced gradient algorithm with retraction and vector transport"
    - "riemannian adaptive optimization methods"
    - "can we gain more from orthogonality regularizations in training deep cnns"
    - "orthogonal weight normalization solution to optimization over multiple dependent stiefel manifolds in deep neural networks"
    - "on orthogonality and learning recurrent networks with long term dependencies"
    - "efficient orthogonal parametrisation of recurrent neural networks using householder reflections"
    - "recurrent orthogonal networks and long memory tasks"
    - "tunable efficient unitary neural networks eunn and their application to rnns"

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)