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: "cheap orthogonal constraints in neural networks a simple parametrization of the orthogonal and unitary group"
  Abstract: We introduce a novel approach to perform first-order optimization with orthogonal and unitary constraints. This approach is based on a parametrization stemming from Lie group theory through the exponential map. The parametrization transforms the constrained optimization problem into an unconstrained one over a Euclidean space, for which common first-order optimization methods can be used. The theoretical results presented are general enough to cover the special orthogonal group, the unitary group and, in general, any connected compact Lie group. We discuss how this and other parametrizations can be computed efficiently through an implementation trick, making numerically complex parametrizations usable at a negligible runtime cost in neural networks. In particular, we apply our results to RNNs with orthogonal recurrent weights, yielding a new architecture called expRNN. We demonstrate how our method constitutes a more robust approach to optimization with orthogonal constraints, showing faster, accurate, and more stable convergence in several tasks designed to test RNNs.

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"
    - "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"
    - "sanity checks for saliency maps"

Connected through hyperedge HE-2 (induced by source paper "efficient riemannian optimization on the stiefel manifold via the cayley transform"):
  with members:
    - "riemannian adaptive optimization methods"
    - "can we gain more from orthogonality regularizations in training deep cnns"
    - "riemannian approach to batch normalization"
    - "on orthogonality and learning recurrent networks with long term dependencies"
    - "dizzyrnn reparameterizing recurrent neural networks for norm preserving backpropagation"
    - "wide residual networks"
    - "reducing overfitting in deep networks by decorrelating representations"
    - "unitary evolution recurrent neural networks"

Connected through hyperedge HE-3 (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"
    - "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"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-4 (induced by source paper "eigenvalue normalized recurrent neural networks for short term memory"):
  with members:
    - "non normal recurrent neural network nnrnn learning long time dependencies while improving expressivity with transient dynamics"
    - "improved memory in recurrent neural networks with sequential non normal dynamics"
    - "stabilizing gradients for deep neural networks via efficient svd parameterization"
    - "complex gated recurrent neural networks"
    - "gated orthogonal recurrent units on learning to forget"
    - "fast slow recurrent neural networks"
    - "on orthogonality and learning recurrent networks with long term dependencies"
    - "efficient orthogonal parametrisation of recurrent neural networks using householder reflections"

Connected through hyperedge HE-5 (induced by source paper "non normal recurrent neural network nnrnn learning long time dependencies while improving expressivity with transient dynamics"):
  with members:
    - "towards non saturating recurrent units for modelling long term dependencies"
    - "improved memory in recurrent neural networks with sequential non normal dynamics"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "stabilizing gradients for deep neural networks via efficient svd parameterization"
    - "resurrecting the sigmoid in deep learning through dynamical isometry theory and practice"
    - "efficient orthogonal parametrisation of recurrent neural networks using householder reflections"
    - "tunable efficient unitary neural networks eunn and their application to rnns"
    - "a simple way to initialize recurrent networks of rectified linear units"

Connected through hyperedge HE-6 (induced by source paper "trivializations for gradient based optimization on manifolds"):
  with members:
    - "riemannian adaptive optimization methods"
    - "stabilizing gradients for deep neural networks via efficient svd parameterization"
    - "improving variational auto encoders using householder flow"
    - "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"
    - "a simple way to initialize recurrent networks of rectified linear units"
    - "unitary evolution recurrent neural networks"

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.LG(Machine Learning)