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: "convexified convolutional neural networks"
  Abstract: We describe the class of convexified convolutional neural networks (CCNNs), which capture the parameter sharing of convolutional neural networks in a convex manner. By representing the nonlinear convolutional filters as vectors in a reproducing kernel Hilbert space, the CNN parameters can be represented as a low-rank matrix, which can be relaxed to obtain a convex optimization problem. For learning two-layer convolutional neural networks, we prove that the generalization error obtained by a convexified CNN converges to that of the best possible CNN. For learning deeper networks, we train CCNNs in a layer-wise manner. Empirically, CCNNs achieve performance competitive with CNNs trained by backpropagation, SVMs, fully-connected neural networks, stacked denoising auto-encoders, and other baseline methods.

Connected through hyperedge HE-1 (induced by source paper "generalised lipschitz regularisation equals distributional robustness"):
  with members:
    - "naive feature selection sparsity in naive bayes"
    - "sorting out lipschitz function approximation"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "spectral normalization for generative adversarial networks"
    - "generalizable adversarial training via spectral normalization"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "universal adversarial perturbations"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-2 (induced by source paper "tight sample complexity of learning one hidden layer convolutional neural networks"):
  with members:
    - "an improved analysis of training over parameterized deep neural networks"
    - "generalization error bounds of gradient descent for learning over parameterized deep relu networks"
    - "on exact computation with an infinitely wide neural net"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "generalization bounds of stochastic gradient descent for wide and deep neural networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"

Connected through hyperedge HE-3 (induced by source paper "learning one hidden layer neural networks via provable gradient descent with random initialization"):
  with members:
    - "nonconvex demixing from bilinear measurements"
    - "the global optimization geometry of shallow linear neural networks"
    - "nonconvex matrix factorization from rank one measurements"
    - "blind over the air computation and data fusion via provable wirtinger flow"
    - "a convergence analysis of gradient descent for deep linear neural networks"
    - "theoretical insights into the optimization landscape of over parameterized shallow neural networks"
    - "how to escape saddle points efficiently"
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"

Connected through hyperedge HE-4 (induced by source paper "svm based deep stacking networks"):
  with members:
    - "deep forest towards an alternative to deep neural networks"
    - "sparse deep stacking network for image classification"
    - "pcanet a simple deep learning baseline for image classification"
    - "deep learning using linear support vector machines"
    - "representation learning a review and new perspectives"
    - "issues in stacked generalization"

Connected through hyperedge HE-5 (induced by source paper "deep frank wolfe for neural network optimization"):
  with members:
    - "on the convergence of adam and beyond"
    - "smooth loss functions for deep top k classification"
    - "faster convergence generalization in dnns"
    - "online learning rate adaptation with hypergradient descent"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "exploring generalization in deep learning"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "learned optimizers that scale and generalize"

Connected through hyperedge HE-6 (induced by source paper "a provably correct algorithm for deep learning that actually works"):
  with members:
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "recovery guarantees for one hidden layer neural networks"
    - "sgd learns the conjugate kernel class of the network"
    - "an analytical formula of population gradient for two layered relu network and its applications in convergence and critical point analysis"
    - "sgd learns over parameterized networks that provably generalize on linearly separable data"
    - "when is a convolutional filter easy to learn"
    - "deep learning and hierarchal generative models"
    - "learning halfspaces and neural networks with random initialization"

Connected through hyperedge HE-7 (induced by source paper "convex relaxations of convolutional neural nets"):
  with members:
    - "learning one convolutional layer with overlapping patches"
    - "lifted neural networks"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "learning relus via gradient descent"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "reliably learning the relu in polynomial time"
    - "randomized sketches of convex programs with sharp guarantees"
    - "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising"

Connected through hyperedge HE-8 (induced by source paper "federated multi task learning"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "cocoa a general framework for communication efficient distributed optimization"
    - "distributed multi task relationship learning"
    - "communication efficient learning of deep networks from decentralized data"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "asynchronous multi task learning"
    - "distributed multi task learning with shared representation"
    - "tensor switching 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)