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: "on the optimization of deep networks implicit acceleration by overparameterization"
  Abstract: Conventional wisdom in deep learning states that increasing depth improves expressiveness but complicates optimization. This paper suggests that, sometimes, increasing depth can speed up optimization. The effect of depth on optimization is decoupled from expressiveness by focusing on settings where additional layers amount to overparameterization - linear neural networks, a well-studied model. Theoretical analysis, as well as experiments, show that here depth acts as a preconditioner which may accelerate convergence. Even on simple convex problems such as linear regression with $\ell_p$ loss, $p>2$, gradient descent can benefit from transitioning to a non-convex overparameterized objective, more than it would from some common acceleration schemes. We also prove that it is mathematically impossible to obtain the acceleration effect of overparametrization via gradients of any regularizer.

Connected through hyperedge HE-1 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers"):
  with members:
    - "scaling laws for neural language models"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "are sixteen heads really better than one"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "checkmate breaking the memory wall with optimal tensor rematerialization"
    - "megatron lm training multi billion parameter language models using model parallelism"

Connected through hyperedge HE-2 (induced by source paper "a deep conditioning treatment of neural networks"):
  with members:
    - "backward feature correction how deep learning performs deep learning"
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "two models of double descent for weak features"
    - "size free generalization bounds for convolutional neural networks"
    - "an improved analysis of training over parameterized deep neural networks"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "implicit regularization in deep matrix factorization"
    - "on the learnability of deep random networks"

Connected through hyperedge HE-3 (induced by source paper "on the global convergence of training deep linear resnets"):
  with members:
    - "provable benefit of orthogonal initialization in optimizing deep linear networks"
    - "an improved analysis of training over parameterized deep neural networks"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "width provably matters in optimization for deep linear neural networks"
    - "on learning over parameterized neural networks a functional approximation prospective"
    - "algorithm dependent generalization bounds for overparameterized deep residual networks"
    - "training over parameterized deep resnet is almost as easy as training a two layer network"
    - "on exact computation with an infinitely wide neural net"

Connected through hyperedge HE-4 (induced by source paper "mse optimal neural network initialization via layer fusion"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "channel charting locating users within the radio environment using channel state information"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "a convergence theory for deep learning via over parameterization"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-5 (induced by source paper "lookahead a far sighted alternative of magnitude based pruning"):
  with members:
    - "backpack packing more into backprop"
    - "centripetal sgd for pruning very deep convolutional networks with complicated structure"
    - "the state of sparsity in deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "the singular values of convolutional layers"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "algorithmic regularization in learning deep homogeneous models layers are automatically balanced"

Connected through hyperedge HE-6 (induced by source paper "unique properties of wide minima in deep networks"):
  with members:
    - "a tail index analysis of stochastic gradient noise in deep neural networks"
    - "implicit regularization of normalization methods"
    - "gradient descent maximizes the margin of homogeneous neural networks"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "gradient descent aligns the layers of deep linear networks"
    - "step size matters in deep learning"
    - "algorithmic regularization in learning deep homogeneous models layers are automatically balanced"
    - "three factors influencing minima in sgd"

Connected through hyperedge HE-7 (induced by source paper "convex duality of deep neural networks"):
  with members:
    - "width provably matters in optimization for deep linear neural networks"
    - "implicit regularization in deep matrix factorization"
    - "exponential convergence time of gradient descent for one dimensional deep linear neural networks"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "gradient descent aligns the layers of deep linear networks"
    - "a convergence analysis of gradient descent for deep linear neural networks"
    - "deep linear neural networks with arbitrary loss all local minima are global"
    - "in search of the real inductive bias on the role of implicit regularization in deep learning"

Connected through hyperedge HE-8 (induced by source paper "a generalization theory of gradient descent for learning over parameterized deep relu networks"):
  with members:
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "small nonlinearities in activation functions create bad local minima in 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"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "towards understanding the role of over parametrization in generalization of 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)