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: "fenchel lifted networks a lagrange relaxation of neural network training"
  Abstract: Despite the recent successes of deep neural networks, the corresponding training problem remains highly non-convex and difficult to optimize. Classes of models have been proposed that introduce greater structure to the objective function at the cost of lifting the dimension of the problem. However, these lifted methods sometimes perform poorly compared to traditional neural networks. In this paper, we introduce a new class of lifted models, Fenchel lifted networks, that enjoy the same benefits as previous lifted models, without suffering a degradation in performance over classical networks. Our model represents activation functions as equivalent biconvex constraints and uses Lagrange Multipliers to arrive at a rigorous lower bound of the traditional neural network training problem. This model is efficiently trained using block-coordinate descent and is parallelizable across data points and/or layers. We compare our model against standard fully connected and convolutional networks and show that we are able to match or beat their performance.

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"
    - "a convergence analysis of gradient descent for deep linear neural networks"

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"
    - "lifted neural networks"
    - "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"
    - "lifted neural networks"
    - "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 "a convergence analysis of nonlinearly constrained admm in deep learning"):
  with members:
    - "deep component analysis via alternating direction neural networks"
    - "alternating direction method of multipliers for sparse convolutional neural networks"
    - "training neural networks without gradients a scalable admm approach"
    - "constructive neural network learning"
    - "adam a method for stochastic optimization"

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)