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: "deep supervised learning using local errors"
  Abstract: Error backpropagation is a highly effective mechanism for learning high-quality hierarchical features in deep networks. Updating the features or weights in one layer, however, requires waiting for the propagation of error signals from higher layers. Learning using delayed and non-local errors makes it hard to reconcile backpropagation with the learning mechanisms observed in biological neural networks as it requires the neurons to maintain a memory of the input long enough until the higher-layer errors arrive. In this paper, we propose an alternative learning mechanism where errors are generated locally in each layer using fixed, random auxiliary classifiers. Lower layers could thus be trained independently of higher layers and training could either proceed layer by layer, or simultaneously in all layers using local error information. We address biological plausibility concerns such as weight symmetry requirements and show that the proposed learning mechanism based on fixed, broad, and random tuning of each neuron to the classification categories outperforms the biologically-motivated feedback alignment learning technique on the MNIST, CIFAR10, and SVHN datasets, approaching the performance of standard backpropagation. Our approach highlights a potential biological mechanism for the supervised, or task-dependent, learning of feature hierarchies. In addition, we show that it is well suited for learning deep networks in custom hardware where it can drastically reduce memory traffic and data communication overheads.

Connected through hyperedge HE-1 (induced by source paper "spiking neural networks for inference and learning a memristor based design perspective"):
  with members:
    - "on resistive memories one step row readout technique and sensing circuitry"
    - "biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets"
    - "surrogate gradient learning in spiking neural networks"
    - "contrastive hebbian learning with random feedback weights"
    - "mitigating asymmetric nonlinear weight update effects in hardware neural network based on analog resistive synapse"
    - "synaptic plasticity dynamics for deep continuous local learning"
    - "slayer spike layer error reassignment in time"
    - "deep neural network optimized to resistive memory with nonlinear current voltage characteristics"

Connected through hyperedge HE-2 (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"
    - "revisiting distributed synchronous sgd"
    - "shake shake regularization"

Connected through hyperedge HE-3 (induced by source paper "surrogate gradient learning in spiking neural networks"):
  with members:
    - "biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets"
    - "deep learning in spiking neural networks"
    - "deep networks incorporating spiking neural dynamics"
    - "synaptic plasticity dynamics for deep continuous local learning"
    - "long short term memory and learning to learn in networks of spiking neurons"
    - "slayer spike layer error reassignment in time"
    - "smplr deep smpl reverse for 3d human pose and shape recovery"
    - "temporally efficient deep learning with spikes"

Connected through hyperedge HE-4 (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"
    - "dynamic routing between capsules"
    - "decoupled neural interfaces using synthetic gradients"
    - "training neural networks without gradients a scalable admm approach"

Connected through hyperedge HE-5 (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"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "a theory of local learning the learning channel and the optimality of backpropagation"
    - "learning in the machine random backpropagation and the deep learning channel"
    - "tensorflow a system for large scale machine learning"
    - "very deep convolutional networks for large scale image recognition"
    - "difference target propagation"

Connected through hyperedge HE-6 (induced by source paper "error triggered three factor learning dynamics for crossbar arrays"):
  with members:
    - "surrogate gradient learning in spiking neural networks"
    - "a neuromorphic systems approach to in memory computing with non ideal memristive devices from mitigation to exploitation"
    - "synaptic plasticity dynamics for deep continuous local learning"
    - "smplr deep smpl reverse for 3d human pose and shape recovery"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "learning in the machine the symmetries of the deep learning channel"

Connected through hyperedge HE-7 (induced by source paper "synaptic plasticity dynamics for deep continuous local learning decolle"):
  with members:
    - "surrogate gradient learning in spiking neural networks"
    - "long short term memory and learning to learn in networks of spiking neurons"
    - "slayer spike layer error reassignment in time"
    - "learning in the machine the symmetries of the deep learning channel"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "decoupled neural interfaces using synthetic gradients"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "training deep spiking neural networks using backpropagation"

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)