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: "reviving and improving recurrent back propagation"
  Abstract: In this paper, we revisit the recurrent back-propagation (RBP) algorithm, discuss the conditions under which it applies as well as how to satisfy them in deep neural networks. We show that RBP can be unstable and propose two variants based on conjugate gradient on the normal equations (CG-RBP) and Neumann series (Neumann-RBP). We further investigate the relationship between Neumann-RBP and back propagation through time (BPTT) and its truncated version (TBPTT). Our Neumann-RBP has the same time complexity as TBPTT but only requires constant memory, whereas TBPTT's memory cost scales linearly with the number of truncation steps. We examine all RBP variants along with BPTT and TBPTT in three different application domains: associative memory with continuous Hopfield networks, document classification in citation networks using graph neural networks and hyperparameter optimization for fully connected networks. All experiments demonstrate that RBPs, especially the Neumann-RBP variant, are efficient and effective for optimizing convergent recurrent neural networks.

Connected through hyperedge HE-1 (induced by source paper "equilibrated recurrent neural network neuronal time delayed self feedback improves accuracy and stability"):
  with members:
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "approximating real time recurrent learning with random kronecker factors"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "reversible recurrent neural networks"
    - "stabilizing gradients for deep neural networks via efficient svd parameterization"
    - "simple recurrent units for highly parallelizable recurrence"
    - "attention is all you need"
    - "unbiased online recurrent optimization"

Connected through hyperedge HE-2 (induced by source paper "learning to remember from a multi task teacher"):
  with members:
    - "backpropamine training self modifying neural networks with differentiable neuromodulated plasticity"
    - "overcoming multi model forgetting"
    - "meta learning representations for continual learning"
    - "understanding and correcting pathologies in the training of learned optimizers"
    - "differentiable plasticity training plastic neural networks with backpropagation"
    - "generative replay with feedback connections as a general strategy for continual learning"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "packnet adding multiple tasks to a single network by iterative pruning"

Connected through hyperedge HE-3 (induced by source paper "optimal kronecker sum approximation of real time recurrent learning"):
  with members:
    - "on the variance of unbiased online recurrent optimization"
    - "approximating real time recurrent learning with random kronecker factors"
    - "unbiasing truncated backpropagation through time"
    - "sparse attentive backtracking temporal creditassignment through reminding"
    - "an analysis of neural language modeling at multiple scales"
    - "on the state of the art of evaluation in neural language models"
    - "attention is all you need"
    - "unbiased online recurrent optimization"

Connected through hyperedge HE-4 (induced by source paper "convolutional bipartite attractor networks"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "learning attractor dynamics for generative memory"
    - "fast and accurate image super resolution with deep laplacian pyramid networks"
    - "density estimation using real nvp"
    - "dense associative memory for pattern recognition"
    - "deeply recursive convolutional network for image super resolution"

Connected through hyperedge HE-5 (induced by source paper "incremental few shot learning with attention attractor networks"):
  with members:
    - "a closer look at few shot classification"
    - "understanding short horizon bias in stochastic meta optimization"
    - "group normalization"
    - "meta learning with differentiable closed form solvers"
    - "low shot learning from imaginary data"
    - "dynamic few shot visual learning without forgetting"
    - "end to end incremental learning"
    - "low shot learning with imprinted weights"

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)