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: "how important is weight symmetry in backpropagation"
  Abstract: Gradient backpropagation (BP) requires symmetric feedforward and feedback connections -- the same weights must be used for forward and backward passes. This "weight transport problem" (Grossberg 1987) is thought to be one of the main reasons to doubt BP's biologically plausibility. Using 15 different classification datasets, we systematically investigate to what extent BP really depends on weight symmetry. In a study that turned out to be surprisingly similar in spirit to Lillicrap et al.'s demonstration (Lillicrap et al. 2014) but orthogonal in its results, our experiments indicate that: (1) the magnitudes of feedback weights do not matter to performance (2) the signs of feedback weights do matter -- the more concordant signs between feedforward and their corresponding feedback connections, the better (3) with feedback weights having random magnitudes and 100% concordant signs, we were able to achieve the same or even better performance than SGD. (4) some normalizations/stabilizations are indispensable for such asymmetric BP to work, namely Batch Normalization (BN) (Ioffe and Szegedy 2015) and/or a "Batch Manhattan" (BM) update rule.

Connected through hyperedge HE-1 (induced by source paper "a deep unsupervised feature learning spiking neural network with binarized classification layers for emnist classification"):
  with members:
    - "a spiking network for inference of relations trained with neuromorphic backpropagation"
    - "deep convolutional spiking neural networks for image classification"
    - "temporal coding in spiking neural networks with alpha synaptic function"
    - "s4nn temporal backpropagation for spiking neural networks with one spike per neuron"
    - "towards scalable efficient and accurate deep spiking neural networks with backward residual connections stochastic softmax and hybridization"
    - "spyketorch efficient simulation of convolutional spiking neural networks with at most one spike per neuron"
    - "dendritic inspired processing enables bio plausible stdp in compound binary synapses"
    - "hybrid macro micro level backpropagation for training deep spiking neural networks"

Connected through hyperedge HE-2 (induced by source paper "questions to guide the future of artificial intelligence research"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "one pixel attack for fooling deep neural networks"
    - "learning in the machine to share or not to share"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "relational recurrent neural networks"
    - "deep learning a critical appraisal"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "hindsight experience replay"

Connected through hyperedge HE-3 (induced by source paper "temporal coding in spiking neural networks with alpha synaptic function"):
  with members:
    - "deep learning in spiking neural networks"
    - "enabling spike based backpropagation in state of the art deep neural network architectures"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "spiking deep residual network"
    - "evolutionary neural hybrid agents for architecture search"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "long short term memory and learning to learn in networks of spiking neurons"
    - "spatio temporal backpropagation for training high performance spiking neural networks"

Connected through hyperedge HE-4 (induced by source paper "deep convolutional spiking neural networks for image classification"):
  with members:
    - "deep learning in spiking neural networks"
    - "stimulating stdp to exploit locality for lifelong learning without catastrophic forgetting"
    - "dendritic inspired processing enables bio plausible stdp in compound binary synapses"
    - "hybrid macro micro level backpropagation for training deep spiking neural networks"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "bio inspired unsupervised learning of visual features leads to robust invariant object recognition"
    - "training deep spiking neural networks using backpropagation"

Connected through hyperedge HE-5 (induced by source paper "conducting credit assignment by aligning local representations"):
  with members:
    - "learning to adapt by minimizing discrepancy"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "decoupled neural interfaces using synthetic gradients"
    - "piecewise latent variables for neural variational text processing"
    - "a modular theory of feature learning"
    - "all you need is a good init"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-6 (induced by source paper "biologically motivated algorithms for propagating local target representations"):
  with members:
    - "multi layered gradient boosting decision trees"
    - "conducting credit assignment by aligning local representations"
    - "learning to adapt by minimizing discrepancy"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "decoupled neural interfaces using synthetic gradients"
    - "towards biologically plausible deep learning"
    - "online semi supervised learning with deep hybrid boltzmann machines and denoising autoencoders"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (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"
    - "learning in the machine random backpropagation and the deep learning channel"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "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-8 (induced by source paper "deep learning with asymmetric connections and hebbian updates"):
  with members:
    - "a biologically plausible learning rule for deep learning in the brain"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "difference target propagation"
    - "exact solutions to the nonlinear dynamics of learning in deep linear 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)