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: "dynamical isometry and a mean field theory of lstms and grus"
  Abstract: Training recurrent neural networks (RNNs) on long sequence tasks is plagued with difficulties arising from the exponential explosion or vanishing of signals as they propagate forward or backward through the network. Many techniques have been proposed to ameliorate these issues, including various algorithmic and architectural modifications. Two of the most successful RNN architectures, the LSTM and the GRU, do exhibit modest improvements over vanilla RNN cells, but they still suffer from instabilities when trained on very long sequences. In this work, we develop a mean field theory of signal propagation in LSTMs and GRUs that enables us to calculate the time scales for signal propagation as well as the spectral properties of the state-to-state Jacobians. By optimizing these quantities in terms of the initialization hyperparameters, we derive a novel initialization scheme that eliminates or reduces training instabilities. We demonstrate the efficacy of our initialization scheme on multiple sequence tasks, on which it enables successful training while a standard initialization either fails completely or is orders of magnitude slower. We also observe a beneficial effect on generalization performance using this new initialization.

Connected through hyperedge HE-1 (induced by source paper "provable benefit of orthogonal initialization in optimizing deep linear networks"):
  with members:
    - "width provably matters in optimization for deep linear neural networks"
    - "exponential convergence time of gradient descent for one dimensional deep linear neural networks"
    - "stable architectures for deep neural networks"
    - "a convergence analysis of gradient descent for deep linear neural networks"
    - "resurrecting the sigmoid in deep learning through dynamical isometry theory and practice"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "rezero is all you need fast convergence at large depth"):
  with members:
    - "batch normalization biases deep residual networks towards shallow paths"
    - "on layer normalization in the transformer architecture"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "character level language modeling with deeper self attention"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "resurrecting the sigmoid in deep learning through dynamical isometry theory and practice"
    - "self normalizing neural networks"
    - "mean field residual networks on the edge of chaos"

Connected through hyperedge HE-3 (induced by source paper "deep learning theory review an optimal control and dynamical systems perspective"):
  with members:
    - "a tail index analysis of stochastic gradient noise in deep neural networks"
    - "a mean field theory of batch normalization"
    - "where is the information in a deep neural network"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "one shot pruning of recurrent neural networks by jacobian spectrum evaluation"):
  with members:
    - "the state of sparsity in deep neural networks"
    - "trace norm regularization and faster inference for embedded speech recognition rnns"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "snip single shot network pruning based on connection sensitivity"
    - "grow and prune compact fast and accurate lstms"
    - "groupreduce block wise low rank approximation for neural language model shrinking"
    - "block sparse recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "disentangling trainability and generalization in deep learning"):
  with members:
    - "dynamics of deep neural networks and neural tangent hierarchy"
    - "freeze and chaos for dnns an ntk view of batch normalization checkerboard and boundary effects"
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "a mean field theory of batch normalization"
    - "finite depth and width corrections to the neural tangent kernel"
    - "on exact computation with an infinitely wide neural net"
    - "transfusion understanding transfer learning for medical imaging"
    - "gradient descent finds global minima of deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "a mean field theory of batch normalization"):
  with members:
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "towards understanding regularization in batch normalization"
    - "understanding batch normalization"
    - "the nonlinearity coefficient predicting overfitting in deep neural networks"
    - "resurrecting the sigmoid in deep learning through dynamical isometry theory and practice"
    - "the shattered gradients problem if resnets are the answer then what is the question"
    - "comparison of batch normalization and weight normalization algorithms for the large scale image classification"
    - "a bayesian perspective on generalization and stochastic gradient descent"

Connected through hyperedge HE-7 (induced by source paper "mean field theory for deep dropout networks digging up gradient backpropagation deeply"):
  with members:
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "a mean field theory of batch normalization"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "resurrecting the sigmoid in deep learning through dynamical isometry theory and practice"
    - "mean field residual networks on the edge of chaos"
    - "toward deeper understanding of neural networks the power of initialization and a dual view on expressivity"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)