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: "learning in the machine random backpropagation and the deep learning channel"
  Abstract: Random backpropagation (RBP) is a variant of the backpropagation algorithm for training neural networks, where the transpose of the forward matrices are replaced by fixed random matrices in the calculation of the weight updates. It is remarkable both because of its effectiveness, in spite of using random matrices to communicate error information, and because it completely removes the taxing requirement of maintaining symmetric weights in a physical neural system. To better understand random backpropagation, we first connect it to the notions of local learning and learning channels. Through this connection, we derive several alternatives to RBP, including skipped RBP (SRPB), adaptive RBP (ARBP), sparse RBP, and their combinations (e.g. ASRBP) and analyze their computational complexity. We then study their behavior through simulations using the MNIST and CIFAR-10 bechnmark datasets. These simulations show that most of these variants work robustly, almost as well as backpropagation, and that multiplication by the derivatives of the activation functions is important. As a follow-up, we study also the low-end of the number of bits required to communicate error information over the learning channel. We then provide partial intuitive explanations for some of the remarkable properties of RBP and its variations. Finally, we prove several mathematical results, including the convergence to fixed points of linear chains of arbitrary length, the convergence to fixed points of linear autoencoders with decorrelated data, the long-term existence of solutions for linear systems with a single hidden layer and convergence in special cases, and the convergence to fixed points of non-linear chains, when the derivative of the activation functions is included.

Connected through hyperedge HE-1 (induced by source paper "biologically plausible deep learning but how far can we go with shallow networks"):
  with members:
    - "unsupervised learning by competing hidden units"
    - "deep learning in spiking neural networks"
    - "putting a bug in ml the moth olfactory network learns to read mnist"
    - "error forward propagation reusing feedforward connections to propagate errors in deep learning"
    - "feedback alignment in deep convolutional networks"
    - "regularized evolution for image classifier architecture search"
    - "a biologically plausible learning rule for deep learning in the brain"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"

Connected through hyperedge HE-2 (induced by source paper "learning in the machine to share or not to share"):
  with members:
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "learning in the machine the symmetries of the deep learning channel"
    - "deep learning in neural networks"
    - "deep residual learning for image recognition"
    - "training very deep networks"
    - "going deeper with convolutions"
    - "difference target propagation"
    - "learning spatiotemporal features with 3d convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "direct feedback alignment with sparse connections for local learning"):
  with members:
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "understanding reuse performance and hardware cost of dnn dataflows a data centric approach"
    - "deep supervised learning using local errors"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "a theory of local learning the learning channel and the optimality of backpropagation"
    - "tensorflow a system for large scale machine learning"
    - "very deep convolutional networks for large scale image recognition"
    - "difference target propagation"

Connected through hyperedge HE-4 (induced by source paper "feedback alignment in deep convolutional networks"):
  with members:
    - "biologically plausible learning algorithms can scale to large datasets"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "how important is weight symmetry in backpropagation"
    - "how auto encoders could provide credit assignment in deep networks via target propagation"
    - "adam a method for stochastic optimization"
    - "difference target propagation"
    - "striving for simplicity the all convolutional net"

Connected through hyperedge HE-5 (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"
    - "understanding synthetic gradients and decoupled neural interfaces"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "learning to adapt by minimizing discrepancy"):
  with members:
    - "learning simpler language models with the differential state framework"
    - "unbiased online recurrent optimization"
    - "training language models using target propagation"
    - "exploiting spatio temporal structure with recurrent winner take all networks"
    - "decoupled neural interfaces using synthetic gradients"
    - "piecewise latent variables for neural variational text processing"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "a modular theory of feature learning"

Connected through hyperedge HE-7 (induced by source paper "neural and synaptic array transceiver a brain inspired computing framework for embedded learning"):
  with members:
    - "contrastive hebbian learning with random feedback weights"
    - "membrane dependent neuromorphic learning rule for unsupervised spike pattern detection"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "forward table based presynaptic event triggered spike timing dependent plasticity"
    - "stochastic synapses enable efficient brain inspired learning machines"
    - "supervised learning based on temporal coding in spiking neural networks"
    - "convolutional networks for fast energy efficient neuromorphic computing"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-8 (induced by source paper "learning in the machine the symmetries of the deep learning channel"):
  with members:
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "deep reinforcement learning from self play in imperfect information games"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "a theory of local learning the learning channel and the optimality of backpropagation"
    - "asynchronous methods for deep reinforcement learning"
    - "mapping generative models onto a network of digital spiking neurons"
    - "increasing the action gap new operators for reinforcement learning"
    - "continuous control with deep reinforcement learning"

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)