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: "kickback cuts backprop s red tape biologically plausible credit assignment in neural networks"
  Abstract: Error backpropagation is an extremely effective algorithm for assigning credit in artificial neural networks. However, weight updates under Backprop depend on lengthy recursive computations and require separate output and error messages -- features not shared by biological neurons, that are perhaps unnecessary. In this paper, we revisit Backprop and the credit assignment problem. We first decompose Backprop into a collection of interacting learning algorithms; provide regret bounds on the performance of these sub-algorithms; and factorize Backprop's error signals. Using these results, we derive a new credit assignment algorithm for nonparametric regression, Kickback, that is significantly simpler than Backprop. Finally, we provide a sufficient condition for Kickback to follow error gradients, and show that Kickback matches Backprop's performance on real-world regression benchmarks.

Connected through hyperedge HE-1 (induced by source paper "sideways depth parallel training of video models"):
  with members:
    - "backprop diffusion is biologically plausible"
    - "decoupled greedy learning of cnns"
    - "dynamonet dynamic action and motion network"
    - "putting an end to end to end gradient isolated learning of representations"
    - "scsampler sampling salient clips from video for efficient action recognition"
    - "rethinking imagenet pre training"
    - "learning visual question answering by bootstrapping hard attention"
    - "massively parallel video networks"

Connected through hyperedge HE-2 (induced by source paper "the hsic bottleneck deep learning without back propagation"):
  with members:
    - "on mutual information maximization for representation learning"
    - "error forward propagation reusing feedforward connections to propagate errors in deep learning"
    - "biologically plausible learning algorithms can scale to large datasets"
    - "feedback alignment in deep convolutional networks"
    - "a biologically plausible learning rule for deep learning in the brain"
    - "intriguing properties of randomly weighted networks generalizing while learning next to nothing"
    - "gradient target propagation"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"

Connected through hyperedge HE-3 (induced by source paper "putting an end to end to end gradient isolated learning of representations"):
  with members:
    - "data efficient image recognition with contrastive predictive coding"
    - "greedy layerwise learning can scale to imagenet"
    - "error forward propagation reusing feedforward connections to propagate errors in deep learning"
    - "biologically plausible learning algorithms can scale to large datasets"
    - "auto encoding total correlation explanation"
    - "i revnet deep invertible networks"
    - "information theoretic co training"
    - "conducting credit assignment by aligning local representations"

Connected through hyperedge HE-4 (induced by source paper "fully decoupled neural network learning using delayed gradients"):
  with members:
    - "decoupled greedy learning of cnns"
    - "beyond data and model parallelism for deep neural networks"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "training neural networks using features replay"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "deep supervised learning using local errors"
    - "revisiting distributed synchronous sgd"

Connected through hyperedge HE-5 (induced by source paper "associated learning decomposing end to end backpropagation based on auto encoders and target propagation"):
  with members:
    - "decoupled parallel backpropagation with convergence guarantee"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "training neural networks using features replay"
    - "biologically motivated algorithms for propagating local target representations"
    - "conducting credit assignment by aligning local representations"
    - "deep supervised learning using local errors"
    - "dynamic routing between capsules"
    - "decoupled neural interfaces using synthetic gradients"

Connected through hyperedge HE-6 (induced by source paper "network parameter learning using nonlinear transforms local representation goals and local propagation constraints"):
  with members:
    - "dense and diverse capsule networks making the capsules learn better"
    - "sgdr stochastic gradient descent with restarts"
    - "training neural networks without gradients a scalable admm approach"
    - "deep learning in neural networks"
    - "adam a method for stochastic optimization"
    - "practical recommendations for gradient based training of deep architectures"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"

Connected through hyperedge HE-7 (induced by source paper "continual learning of recurrent neural networks by locally aligning distributed representations"):
  with members:
    - "a neural temporal model for human motion prediction"
    - "sparse attentive backtracking long range credit assignment in recurrent networks"
    - "approximating real time recurrent learning with random kronecker factors"
    - "biologically motivated algorithms for propagating local target representations"
    - "conducting credit assignment by aligning local representations"
    - "lstm a search space odyssey"
    - "learning to adapt by minimizing discrepancy"
    - "learning simpler language models with the differential state framework"

Connected through hyperedge HE-8 (induced by source paper "training neural networks using features replay"):
  with members:
    - "decoupled parallel backpropagation with convergence guarantee"
    - "revisiting distributed synchronous sgd"
    - "benefits of depth in neural networks"
    - "densely connected convolutional networks"
    - "asynchronous methods for deep reinforcement learning"
    - "decoupled neural interfaces using synthetic gradients"
    - "training neural networks without gradients a scalable admm approach"
    - "the power of depth for feedforward 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.LG(Machine Learning)