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: "invariance of weight distributions in rectified mlps"
  Abstract: An interesting approach to analyzing and developing tools for neural networks that has received renewed attention is to examine the equivalent kernel of the neural network. This is based on the fact that a fully connected feedforward network with one hidden layer, a certain weight distribution, an activation function, and an infinite number of neurons is a mapping that can be viewed as a projection into a Hilbert space. We show that the equivalent kernel of an MLP with ReLU or Leaky ReLU activations for all rotationally-invariant weight distributions is the same, generalizing a previous result that required Gaussian weight distributions. We derive the equivalent kernel for these cases. In deep networks, the equivalent kernel approaches a pathological fixed point, which can be used to argue why training randomly initialized networks can be difficult. Our results also have implications for weight initialization and the level sets in neural network cost functions.

Connected through hyperedge HE-1 (induced by source paper "avoiding kernel fixed points computing with elu and gelu infinite networks"):
  with members:
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "richer priors for infinitely wide multi layer perceptrons"
    - "exchangeability and kernel invariance in trained mlps"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "searching for activation functions"
    - "sigmoid weighted linear units for neural network function approximation in reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"):
  with members:
    - "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"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "gradient descent aligns the layers of deep linear networks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "stochastic gradient descent optimizes over parameterized deep relu networks"

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"
    - "on the expressive power of deep polynomial 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"

Connected through hyperedge HE-4 (induced by source paper "benefits of jointly training autoencoders an improved neural tangent kernel analysis"):
  with members:
    - "quadratic suffices for over parametrization via matrix chernoff bound"
    - "an improved analysis of training over parameterized deep neural networks"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "memorization in overparameterized autoencoders"
    - "on exact computation with an infinitely wide neural net"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "neural tangent kernel convergence and generalization in neural networks"

Connected through hyperedge HE-5 (induced by source paper "richer priors for infinitely wide multi layer perceptrons"):
  with members:
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "exchangeability and kernel invariance in trained mlps"
    - "swish a self gated activation function"
    - "toward deeper understanding of neural networks the power of initialization and a dual view on expressivity"
    - "deep residual learning for image recognition"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "going deeper with convolutions"

Connected through hyperedge HE-6 (induced by source paper "spectral analysis of kernel and neural embeddings optimization and generalization"):
  with members:
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "deep learning in neural networks"
    - "representation learning a review and new perspectives"
    - "algorithms for learning kernels based on centered alignment"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-7 (induced by source paper "gradient descent provably optimizes over parameterized neural networks"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "algorithmic regularization in learning deep homogeneous models layers are automatically balanced"
    - "on the power of over parametrization in neural networks with quadratic activation"
    - "learning one hidden layer neural networks with landscape design"
    - "learning relus via gradient descent"
    - "optimization landscape and expressivity of deep cnns"

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)