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: "is deeper better only when shallow is good"
  Abstract: Understanding the power of depth in feed-forward neural networks is an ongoing challenge in the field of deep learning theory. While current works account for the importance of depth for the expressive power of neural-networks, it remains an open question whether these benefits are exploited during a gradient-based optimization process. In this work we explore the relation between expressivity properties of deep networks and the ability to train them efficiently using gradient-based algorithms. We give a depth separation argument for distributions with fractal structure, showing that they can be expressed efficiently by deep networks, but not with shallow ones. These distributions have a natural coarse-to-fine structure, and we show that the balance between the coarse and fine details has a crucial effect on whether the optimization process is likely to succeed. We prove that when the distribution is concentrated on the fine details, gradient-based algorithms are likely to fail. Using this result we prove that, at least in some distributions, the success of learning deep networks depends on whether the distribution can be well approximated by shallower networks, and we conjecture that this property holds in general.

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 "better depth width trade offs for neural networks through the lens of dynamical systems"):
  with members:
    - "on the expressive power of deep polynomial neural networks"
    - "benefits of depth in neural networks"
    - "understanding deep neural networks with rectified linear units"
    - "the power of depth for feedforward neural networks"
    - "representation benefits of deep feedforward networks"
    - "super linear gate and super quadratic wire lower bounds for depth two and depth three threshold circuits"

Connected through hyperedge HE-3 (induced by source paper "learning boolean circuits with neural networks"):
  with members:
    - "decoupled greedy learning of cnns"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "id3 learns juntas for smoothed product distributions"
    - "on the learnability of deep random networks"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "on the power and limitations of random features for understanding neural networks"
    - "a comparative analysis of the optimization and generalization property of two layer neural network and random feature models under gradient descent dynamics"
    - "what can resnet learn efficiently going beyond kernels"

Connected through hyperedge HE-4 (induced by source paper "depth width trade offs for relu networks via sharkovsky s theorem"):
  with members:
    - "the route to chaos in routing games population increase drives period doubling instability chaos inefficiency with price of anarchy equal to one"
    - "on the expressive power of deep polynomial neural networks"
    - "benefits of depth in neural networks"
    - "why deep neural networks for function approximation"
    - "understanding deep neural networks with rectified linear units"
    - "the power of depth for feedforward neural networks"
    - "representation benefits of deep feedforward networks"
    - "super linear gate and super quadratic wire lower bounds for depth two and depth three threshold circuits"

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)