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: "improving performance of recurrent neural network with relu nonlinearity"
  Abstract: In recent years significant progress has been made in successfully training recurrent neural networks (RNNs) on sequence learning problems involving long range temporal dependencies. The progress has been made on three fronts: (a) Algorithmic improvements involving sophisticated optimization techniques, (b) network design involving complex hidden layer nodes and specialized recurrent layer connections and (c) weight initialization methods. In this paper, we focus on recently proposed weight initialization with identity matrix for the recurrent weights in a RNN. This initialization is specifically proposed for hidden nodes with Rectified Linear Unit (ReLU) non linearity. We offer a simple dynamical systems perspective on weight initialization process, which allows us to propose a modified weight initialization strategy. We show that this initialization technique leads to successfully training RNNs composed of ReLUs. We demonstrate that our proposal produces comparable or better solution for three toy problems involving long range temporal structure: the addition problem, the multiplication problem and the MNIST classification problem using sequence of pixels. In addition, we present results for a benchmark action recognition problem.

Connected through hyperedge HE-1 (induced by source paper "deep independently recurrent neural network indrnn"):
  with members:
    - "temporal transformer networks joint learning of invariant and discriminative time warping"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "part based graph convolutional network for action recognition"
    - "learning long term dependencies via fourier recurrent units"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "view adaptive neural networks for high performance skeleton based human action recognition"
    - "simple recurrent units for highly parallelizable recurrence"
    - "lstm a search space odyssey"

Connected through hyperedge HE-2 (induced by source paper "rnns evolving on an equilibrium manifold a panacea for vanishing and exploding gradients"):
  with members:
    - "recurrent neural networks in the eye of differential equations"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "frage frequency agnostic word representation"
    - "can recurrent neural networks warp time"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "stabilizing gradients for deep neural networks via efficient svd parameterization"
    - "neural ordinary differential equations"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "inferring dynamical systems with long range dependencies through line attractor regularization"):
  with members:
    - "learning dynamical systems from partial observations"
    - "gated recurrent units viewed through the lens of continuous time dynamical systems"
    - "identifying nonlinear dynamical systems via generative recurrent neural networks with applications to fmri"
    - "non normal recurrent neural network nnrnn learning long time dependencies while improving expressivity with transient dynamics"
    - "state space lstm models with particle mcmc inference"
    - "gated orthogonal recurrent units on learning to forget"
    - "recurrent orthogonal networks and long memory tasks"
    - "a simple way to initialize recurrent networks of rectified linear units"

Connected through hyperedge HE-4 (induced by source paper "on the initialization of long short term memory networks"):
  with members:
    - "training recurrent neural networks robust to incomplete data application to alzheimer s disease progression modeling"
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "the ucr time series archive"
    - "on orthogonality and learning recurrent networks with long term dependencies"
    - "a simple way to initialize recurrent networks of rectified linear units"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-5 (induced by source paper "identifying nonlinear dynamical systems via generative recurrent neural networks with applications to fmri"):
  with members:
    - "synthesis of recurrent neural networks for dynamical system simulation"
    - "a recurrent latent variable model for sequential data"
    - "review fight sample degeneracy and impoverishment in particle filters a review of intelligent approaches"

Connected through hyperedge HE-6 (induced by source paper "independently recurrent neural network indrnn building a longer and deeper rnn"):
  with members:
    - "simple recurrent units for highly parallelizable recurrence"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "lstm a search space odyssey"
    - "pose conditioned spatio temporal attention for human action recognition"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "fast slow recurrent neural networks"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "joint distance maps based action recognition with convolutional neural networks"

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.NE(Neural and Evolutionary Computing)