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 expressive power of deep polynomial neural networks"
  Abstract: We study deep neural networks with polynomial activations, particularly their expressive power. For a fixed architecture and activation degree, a polynomial neural network defines an algebraic map from weights to polynomials. The image of this map is the functional space associated to the network, and it is an irreducible algebraic variety upon taking closure. This paper proposes the dimension of this variety as a precise measure of the expressive power of polynomial neural networks. We obtain several theoretical results regarding this dimension as a function of architecture, including an exact formula for high activation degrees, as well as upper and lower bounds on layer widths in order for deep polynomials networks to fill the ambient functional space. We also present computational evidence that it is profitable in terms of expressiveness for layer widths to increase monotonically and then decrease monotonically. Finally, we link our study to favorable optimization properties when training weights, and we draw intriguing connections with tensor and polynomial decompositions.

Connected through hyperedge HE-1 (induced by source paper "π nets deep polynomial neural networks"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "polygan high order polynomial generators"
    - "on the convergence of adam and beyond"
    - "semantic image synthesis with spatially adaptive normalization"
    - "adversarial training of partially invertible variational autoencoders"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "residual networks of residual networks multilevel residual networks"
    - "mixed link 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:
    - "is deeper better only when shallow is good"
    - "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 "effect of activation functions on the training of overparametrized neural nets"):
  with members:
    - "is it time to swish comparing deep learning activation functions across nlp tasks"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "on exact computation with an infinitely wide neural net"
    - "fine grained analysis of optimization and generalization for overparameterized two layer 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"
    - "activation functions comparison of trends in practice and research for deep learning"

Connected through hyperedge HE-4 (induced by source paper "generating accurate pseudo labels via hermite polynomials for ssl confidently"):
  with members:
    - "trainability and data dependent initialization of over parameterized relu neural networks"
    - "label propagation for deep semi supervised learning"
    - "joint optimization framework for learning with noisy labels"
    - "activation functions comparison of trends in practice and research for deep learning"
    - "saas speed as a supervisor for semi supervised learning"
    - "why relu networks yield high confidence predictions far away from the training data and how to mitigate the problem"
    - "learning one hidden layer neural networks with landscape design"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"

Connected through hyperedge HE-5 (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"
    - "is deeper better only when shallow is good"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "pure and spurious critical points a geometric study of linear networks"):
  with members:
    - "depth creates no more spurious local minima"
    - "small nonlinearities in activation functions create bad local minima in neural networks"
    - "deep linear neural networks with arbitrary loss all local minima are global"
    - "depth creates no bad local minima"
    - "global optimality conditions for deep neural networks"
    - "identity matters in deep learning"
    - "the loss surfaces of multilayer networks"

Connected through hyperedge HE-7 (induced by source paper "learning algebraic models of quantum entanglement"):
  with members:
    - "polynomial regression as an alternative to neural nets"

Connected through hyperedge HE-8 (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"
    - "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"
    - "on the loss landscape of a class of deep neural networks with no bad local valleys"

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)