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: "depth separation for neural networks"
  Abstract: Let $f:\mathbb{S}^{d-1}\times \mathbb{S}^{d-1}\to\mathbb{S}$ be a function of the form $f(\mathbf{x},\mathbf{x}') = g(\langle\mathbf{x},\mathbf{x}'\rangle)$ for $g:[-1,1]\to \mathbb{R}$. We give a simple proof that shows that poly-size depth two neural networks with (exponentially) bounded weights cannot approximate $f$ whenever $g$ cannot be approximated by a low degree polynomial. Moreover, for many $g$'s, such as $g(x)=\sin(\pi d^3x)$, the number of neurons must be $2^{\Omega\left(d\log(d)\right)}$. Furthermore, the result holds w.r.t.\ the uniform distribution on $\mathbb{S}^{d-1}\times \mathbb{S}^{d-1}$. As many functions of the above form can be well approximated by poly-size depth three networks with poly-bounded weights, this establishes a separation between depth two and depth three networks w.r.t.\ the uniform distribution on $\mathbb{S}^{d-1}\times \mathbb{S}^{d-1}$.

Connected through hyperedge HE-1 (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-2 (induced by source paper "a corrective view of neural networks representation memorization and learning"):
  with members:
    - "effect of activation functions on the training of overparametrized neural nets"
    - "quadratic suffices for over parametrization via matrix chernoff bound"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "barron spaces and the compositional function spaces for neural network models"
    - "polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow relu networks"
    - "on the power and limitations of random features for understanding neural networks"
    - "gradient descent finds global minima of deep neural networks"
    - "neural tangent kernel convergence and generalization in neural networks"

Connected through hyperedge HE-3 (induced by source paper "approximation power of random neural networks"):
  with members:
    - "generalization error bounds of gradient descent for learning over parameterized deep relu networks"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "how do infinite width bounded norm networks look in function space"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "a generalization theory of gradient descent for learning over parameterized deep relu 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-4 (induced by source paper "depth separations in neural networks what is actually being separated"):
  with members:
    - "provable limitations of deep learning"
    - "on the complexity of learning neural networks"
    - "failures of gradient based deep learning"
    - "error bounds for approximations with deep relu networks"
    - "distribution specific hardness of learning neural networks"
    - "benefits of depth in neural networks"
    - "why deep neural networks for function approximation"
    - "depth width tradeoffs in approximating natural functions with neural networks"

Connected through hyperedge HE-5 (induced by source paper "is deeper better only when shallow is good"):
  with members:
    - "failures of gradient based deep learning"
    - "sgd learns over parameterized networks that provably generalize on linearly separable data"
    - "bounding and counting linear regions of deep neural networks"
    - "deep vs shallow networks an approximation theory perspective"
    - "benefits of depth in neural networks"
    - "depth width tradeoffs in approximating natural functions with neural networks"
    - "why and when can deep but not shallow networks avoid the curse of dimensionality a review"
    - "the power of depth for feedforward neural networks"

Connected through hyperedge HE-6 (induced by source paper "a lattice based approach to the expressivity of deep relu neural networks"):
  with members:
    - "on the cvp for the root lattices via folding with deep relu neural networks"
    - "neural lattice decoders"
    - "deep learning methods for improved decoding of linear codes"
    - "nearly tight vc dimension and pseudodimension bounds for piecewise linear neural networks"
    - "on deep learning based channel decoding"
    - "benefits of depth in neural networks"
    - "understanding deep neural networks with rectified linear units"
    - "depth width tradeoffs in approximating natural functions with neural networks"

Connected through hyperedge HE-7 (induced by source paper "on the optimization of deep networks implicit acceleration by overparameterization"):
  with members:
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "analysis and design of convolutional networks via hierarchical tensor decompositions"
    - "on the ability of neural nets to express distributions"
    - "spurious local minima are common in two layer relu neural networks"
    - "training input output recurrent neural networks through spectral methods"
    - "identity matters in deep learning"
    - "the power of depth for feedforward neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "limits on representing boolean functions by linear combinations of simple functions thresholds relus and low degree polynomials"):
  with members:
    - "weights at the bottom matter when the top is heavy"
    - "polynomial representations of threshold functions and algorithmic applications"
    - "understanding deep neural networks with rectified linear units"
    - "depth width tradeoffs in approximating natural functions with neural networks"
    - "the power of depth for feedforward neural networks"
    - "super linear gate and super quadratic wire lower bounds for depth two and depth three threshold circuits"
    - "new algorithms and lower bounds for circuits with linear threshold gates"

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)