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: "understanding synthetic gradients and decoupled neural interfaces"
  Abstract: When training neural networks, the use of Synthetic Gradients (SG) allows layers or modules to be trained without update locking - without waiting for a true error gradient to be backpropagated - resulting in Decoupled Neural Interfaces (DNIs). This unlocked ability of being able to update parts of a neural network asynchronously and with only local information was demonstrated to work empirically in Jaderberg et al (2016). However, there has been very little demonstration of what changes DNIs and SGs impose from a functional, representational, and learning dynamics point of view. In this paper, we study DNIs through the use of synthetic gradients on feed-forward networks to better understand their behaviour and elucidate their effect on optimisation. We show that the incorporation of SGs does not affect the representational strength of the learning system for a neural network, and prove the convergence of the learning system for linear and deep linear models. On practical problems we investigate the mechanism by which synthetic gradient estimators approximate the true loss, and, surprisingly, how that leads to drastically different layer-wise representations. Finally, we also expose the relationship of using synthetic gradients to other error approximation techniques and find a unifying language for discussion and comparison.

Connected through hyperedge HE-1 (induced by source paper "decoupled greedy learning of cnns"):
  with members:
    - "greedy layerwise learning can scale to imagenet"
    - "biologically plausible learning algorithms can scale to large datasets"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "latent space policies for hierarchical reinforcement learning"
    - "training neural networks using features replay"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "learning deep resnet blocks sequentially using boosting theory"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-2 (induced by source paper "biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets"):
  with members:
    - "synaptic plasticity dynamics for deep continuous local learning decolle"
    - "synaptic plasticity dynamics for deep continuous local learning"
    - "long short term memory and learning to learn in networks of spiking neurons"
    - "measuring abstract reasoning in neural networks"
    - "lstm a search space odyssey"
    - "deep rewiring training very sparse deep networks"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "learning to reinforcement learn"

Connected through hyperedge HE-3 (induced by source paper "alternating synthetic and real gradients for neural language modeling"):
  with members:
    - "training neural networks using features replay"
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "snapshot ensembles train 1 get m for free"
    - "regularizing and optimizing lstm language models"
    - "sgdr stochastic gradient descent with restarts"
    - "decoupled neural interfaces using synthetic gradients"
    - "quasi recurrent neural networks"
    - "pointer sentinel mixture models"

Connected through hyperedge HE-4 (induced by source paper "reversible recurrent neural networks"):
  with members:
    - "lstm a search space odyssey"
    - "the reversible residual network backpropagation without storing activations"
    - "on the state of the art of evaluation in neural language models"
    - "opennmt open source toolkit for neural machine translation"
    - "regularizing and optimizing lstm language models"
    - "deep architectures for neural machine translation"
    - "improving variational inference with inverse autoregressive flow"
    - "density estimation using real nvp"

Connected through hyperedge HE-5 (induced by source paper "low pass recurrent neural networks a memory architecture for longer term correlation discovery"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "the statistical recurrent unit"
    - "unbiased online recurrent optimization"
    - "neural episodic control"
    - "scale invariant temporal history sith optimal slicing of the past in an uncertain world"
    - "population based training of neural networks"
    - "associative long short term memory"
    - "reinforcement learning with unsupervised auxiliary tasks"

Connected through hyperedge HE-6 (induced by source paper "decoupled parallel backpropagation with convergence guarantee"):
  with members:
    - "benefits of depth in neural networks"
    - "densely connected convolutional networks"
    - "decoupled neural interfaces using synthetic gradients"
    - "training neural networks without gradients a scalable admm approach"
    - "the power of depth for feedforward neural networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"

Connected through hyperedge HE-7 (induced by source paper "network learning with local propagation"):
  with members:
    - "sgdr stochastic gradient descent with restarts"
    - "decoupled neural interfaces using synthetic gradients"
    - "training neural networks without gradients a scalable admm approach"
    - "an overview of gradient descent optimization algorithms"
    - "a critical review of recurrent neural networks for sequence learning"
    - "deep learning in neural networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "deep supervised learning using local errors"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "hardware efficient on line learning through pipelined truncated error backpropagation in binary state networks"
    - "vlsi implementation of deep neural network using integral stochastic computing"
    - "nullhop a flexible convolutional neural network accelerator based on sparse representations of feature maps"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "densely connected convolutional networks"
    - "decoupled neural interfaces using synthetic gradients"
    - "vdnn virtualized deep neural networks for scalable memory efficient neural network design"

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)