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: "lifted neural networks"
  Abstract: We describe a novel family of models of multi- layer feedforward neural networks in which the activation functions are encoded via penalties in the training problem. Our approach is based on representing a non-decreasing activation function as the argmin of an appropriate convex optimiza- tion problem. The new framework allows for algo- rithms such as block-coordinate descent methods to be applied, in which each step is composed of a simple (no hidden layer) supervised learning problem that is parallelizable across data points and/or layers. Experiments indicate that the pro- posed models provide excellent initial guesses for weights for standard neural networks. In addi- tion, the model provides avenues for interesting extensions, such as robustness against noisy in- puts and optimizing over parameters in activation functions.

Connected through hyperedge HE-1 (induced by source paper "effects of depth width and initialization a convergence analysis of layer wise training for deep linear neural networks"):
  with members:
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "greedy layerwise learning can scale to imagenet"
    - "gradient descent finds global minima of deep neural networks"
    - "gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks"
    - "a provably correct algorithm for deep learning that actually works"
    - "stochastic gradient descent optimizes over parameterized deep relu networks"
    - "fenchel lifted networks a lagrange relaxation of neural network training"

Connected through hyperedge HE-2 (induced by source paper "implicit deep learning"):
  with members:
    - "satnet bridging deep learning and logical reasoning using a differentiable satisfiability solver"
    - "deep equilibrium models"
    - "differentiable mpc for end to end planning and control"
    - "lifted proximal operator machines"
    - "neural ordinary differential equations"
    - "fenchel lifted networks a lagrange relaxation of neural network training"
    - "optnet differentiable optimization as a layer in neural networks"
    - "task based end to end model learning in stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "contrastive learning for lifted networks"):
  with members:
    - "lifted proximal operator machines"
    - "fenchel lifted networks a lagrange relaxation of neural network training"
    - "proximal backpropagation"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "training neural networks without gradients a scalable admm approach"
    - "equilibrium propagation bridging the gap between energy based models and backpropagation"
    - "trusting svm for piecewise linear cnns"
    - "efficient training of very deep neural networks for supervised hashing"

Connected through hyperedge HE-4 (induced by source paper "lifted proximal operator machines"):
  with members:
    - "training neural networks without gradients a scalable admm approach"
    - "efficient training of very deep neural networks for supervised hashing"
    - "deep residual learning for image recognition"
    - "equilibrated adaptive learning rates for non convex optimization"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "adam a method for stochastic optimization"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-5 (induced by source paper "fenchel lifted networks a lagrange relaxation of neural network training"):
  with members:
    - "lifted proximal operator machines"
    - "parmac distributed optimisation of nested functions with application to binary autoencoders"
    - "entropy sgd biasing gradient descent into wide valleys"
    - "training neural networks without gradients a scalable admm approach"
    - "efficient training of very deep neural networks for supervised hashing"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "identifying and attacking the saddle point problem in high dimensional non convex optimization"

Connected through hyperedge HE-6 (induced by source paper "convex relaxations of convolutional neural nets"):
  with members:
    - "learning one convolutional layer with overlapping patches"
    - "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"
    - "convexified convolutional neural networks"
    - "randomized sketches of convex programs with sharp guarantees"
    - "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising"

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)