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: "bio inspired unsupervised learning of visual features leads to robust invariant object recognition"
  Abstract: Retinal image of surrounding objects varies tremendously due to the changes in position, size, pose, illumination condition, background context, occlusion, noise, and non-rigid deformations. But despite these huge variations, our visual system is able to invariantly recognize any object in just a fraction of a second. To date, various computational models have been proposed to mimic the hierarchical processing of the ventral visual pathway, with limited success. Here, we show that the association of both biologically inspired network architecture and learning rule significantly improves the models' performance when facing challenging invariant object recognition problems. Our model is an asynchronous feedforward spiking neural network. When the network is presented with natural images, the neurons in the entry layers detect edges, and the most activated ones fire first, while neurons in higher layers are equipped with spike timing-dependent plasticity. These neurons progressively become selective to intermediate complexity visual features appropriate for object categorization. The model is evaluated on 3D-Object and ETH-80 datasets which are two benchmarks for invariant object recognition, and is shown to outperform state-of-the-art models, including DeepConvNet and HMAX. This demonstrates its ability to accurately recognize different instances of multiple object classes even under various appearance conditions (different views, scales, tilts, and backgrounds). Several statistical analysis techniques are used to show that our model extracts class specific and highly informative features.

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 "improving stdp based visual feature learning with whitening"):
  with members:
    - "decorrelated batch normalization"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "a survey of neuromorphic computing and neural networks in hardware"
    - "bio inspired spiking convolutional neural network using layer wise sparse coding and stdp learning"

Connected through hyperedge HE-3 (induced by source paper "deep learning in spiking neural networks"):
  with members:
    - "representation learning using event based stdp"
    - "mt spike a multilayer time based spiking neuromorphic architecture with temporal error backpropagation"
    - "optimal localist and distributed coding of spatiotemporal spike patterns through stdp and coincidence detection"
    - "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"
    - "a spiking network that learns to extract spike signatures from speech signals"
    - "event driven random back propagation enabling neuromorphic deep learning machines"

Connected through hyperedge HE-4 (induced by source paper "s4nn temporal backpropagation for spiking neural networks with one spike per neuron"):
  with members:
    - "recognizing images with at most one spike per neuron"
    - "biologically plausible deep learning but how far can we go with shallow networks"
    - "spikegrad an ann equivalent computation model for implementing backpropagation with spikes"
    - "deep convolutional spiking neural networks for image classification"
    - "deep learning in spiking neural networks"
    - "fast and deep neuromorphic learning with time to first spike coding"
    - "temporal coding in spiking neural networks with alpha synaptic function"
    - "surrogate gradient learning in spiking neural networks"

Connected through hyperedge HE-5 (induced by source paper "deep learning incorporating biologically inspired neural dynamics"):
  with members:
    - "long short term memory and learning to learn in networks of spiking neurons"
    - "spatio temporal backpropagation for training high performance spiking neural networks"
    - "lstm a search space odyssey"
    - "fatiguing stdp learning from spike timing codes in the presence of rate codes"
    - "bp stdp approximating backpropagation using spike timing dependent plasticity"
    - "mask r cnn"
    - "using the output embedding to improve language models"
    - "training deep spiking neural networks using backpropagation"

Connected through hyperedge HE-6 (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"
    - "training deep spiking neural networks using backpropagation"
    - "converting static image datasets to spiking neuromorphic datasets using saccades"

Connected through hyperedge HE-7 (induced by source paper "spyketorch efficient simulation of convolutional spiking neural networks with at most one spike per neuron"):
  with members:
    - "deep convolutional spiking neural networks for image classification"
    - "deep learning in spiking neural networks"
    - "surrogate gradient learning in spiking neural networks"
    - "multi layered spiking neural network with target timestamp threshold adaptation and stdp"
    - "bio inspired digit recognition using reward modulated spike timing dependent plasticity in deep convolutional networks"
    - "mt spike a multilayer time based spiking neuromorphic architecture with temporal error backpropagation"
    - "bindsnet a machine learning oriented spiking neural networks library in python"
    - "stdp based spiking deep convolutional neural networks for object recognition"

Connected through hyperedge HE-8 (induced by source paper "unsupervised visual feature learning with spike timing dependent plasticity how far are we from traditional feature learning approaches"):
  with members:
    - "deep learning in spiking neural networks"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "deep patch learning for weakly supervised object classification and discovery"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "training deep spiking neural networks using backpropagation"
    - "convolutional networks for fast energy efficient neuromorphic computing"
    - "crf learning with cnn features for image segmentation"
    - "unsupervised feature learning with c svddnet"

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.CV(Computer Vision and Pattern Recognition)