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: "learning depth three neural networks in polynomial time"
  Abstract: We give a polynomial-time algorithm for learning neural networks with one hidden layer of sigmoids feeding into any smooth, monotone activation function (e.g., sigmoid or ReLU). We make no assumptions on the structure of the network, and the algorithm succeeds with respect to {\em any} distribution on the unit ball in $n$ dimensions (hidden weight vectors also have unit norm). This is the first assumption-free, provably efficient algorithm for learning neural networks with more than one hidden layer. Our algorithm-- {\em Alphatron}-- is a simple, iterative update rule that combines isotonic regression with kernel methods. It outputs a hypothesis that yields efficient oracle access to interpretable features. It also suggests a new approach to Boolean function learning via smooth relaxations of hard thresholds, sidestepping traditional hardness results from computational learning theory. Along these lines, we give improved results for a number of longstanding problems related to Boolean concept learning, unifying a variety of different techniques. For example, we give the first polynomial-time algorithm for learning intersections of halfspaces with a margin (distribution-free) and the first generalization of DNF learning to the setting of probabilistic concepts (queries; uniform distribution). Finally, we give the first provably correct algorithms for common schemes in multiple-instance learning.

Connected through hyperedge HE-1 (induced by source paper "on the learnability of deep random networks"):
  with members:
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "convergence results for neural networks via electrodynamics"
    - "learning non overlapping convolutional neural networks with multiple kernels"
    - "learning neural networks with two nonlinear layers in polynomial time"
    - "when is a convolutional filter easy to learn"
    - "eigenvalue decay implies polynomial time learnability for neural networks"

Connected through hyperedge HE-2 (induced by source paper "fitting relus via sgd and quantized sgd"):
  with members:
    - "learning relu networks via alternating minimization"
    - "stochastic gradient descent learns state equations with nonlinear activations"
    - "gradiveq vector quantization for bandwidth efficient gradient aggregation in distributed cnn training"
    - "learning one convolutional layer with overlapping patches"
    - "signsgd compressed optimisation for non convex problems"
    - "learning relus via gradient descent"
    - "the power of interpolation understanding the effectiveness of sgd in modern over parametrized learning"
    - "learning neural networks with two nonlinear layers in polynomial time"

Connected through hyperedge HE-3 (induced by source paper "bandit multiclass linear classification efficient algorithms for the separable case"):
  with members:
    - "contextual bandits with surrogate losses margin bounds and efficient algorithms"
    - "logistic regression the importance of being improper"
    - "learning neural networks with two nonlinear layers in polynomial time"
    - "improved regret bounds for oracle based adversarial contextual bandits"
    - "bistro an efficient relaxation based method for contextual bandits"
    - "efficient algorithms for adversarial contextual learning"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "the price of bandit information in multiclass online classification"

Connected through hyperedge HE-4 (induced by source paper "towards a theoretical understanding of hashing based neural nets"):
  with members:
    - "on the convergence rate of training recurrent neural networks"
    - "adversarial examples from cryptographic pseudo random generators"
    - "spectral signatures in backdoor attacks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "the computational complexity of training relu s"
    - "adversarially robust generalization requires more data"
    - "a convergence theory for deep learning via over parameterization"
    - "relative error tensor low rank approximation"

Connected through hyperedge HE-5 (induced by source paper "on the benefit of width for neural networks disappearance of bad basins"):
  with members:
    - "mean field limit of the learning dynamics of multilayer neural networks"
    - "on the expressive power of deep polynomial neural networks"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "roberta a robustly optimized bert pretraining approach"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"

Connected through hyperedge HE-6 (induced by source paper "learning two layer neural networks with symmetric inputs"):
  with members:
    - "end to end learning of a convolutional neural network via deep tensor decomposition"
    - "learning one hidden layer neural networks under general input distributions"
    - "learning one convolutional layer with overlapping patches"
    - "improved learning of one hidden layer convolutional neural networks with overlaps"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "learning one hidden layer neural networks with landscape design"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"

Connected through hyperedge HE-7 (induced by source paper "gradient descent for one hidden layer neural networks polynomial convergence and sq lower bounds"):
  with members:
    - "learning one convolutional layer with overlapping patches"
    - "learning two layer neural networks with symmetric inputs"
    - "on the complexity of learning neural networks"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "sgd learns the conjugate kernel class of the network"
    - "convergence analysis of two layer neural networks with relu activation"
    - "reliably learning the relu in polynomial time"
    - "benefits of depth in neural networks"

Connected through hyperedge HE-8 (induced by source paper "recovering the lowest layer of deep networks with high threshold activations"):
  with members:
    - "learning one hidden layer neural networks with landscape design"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "when is a convolutional filter easy to learn"
    - "eigenvalue decay implies polynomial time learnability for neural networks"
    - "reliably learning the relu in polynomial time"
    - "efficient approaches for escaping higher order saddle points in non convex optimization"
    - "provable bounds for learning some deep representations"

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.DS(Data Structures and Algorithms)