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 networks can resemble human feed forward vision in invariant object recognition"
  Abstract: Deep convolutional neural networks (DCNNs) have attracted much attention recently, and have shown to be able to recognize thousands of object categories in natural image databases. Their architecture is somewhat similar to that of the human visual system: both use restricted receptive fields, and a hierarchy of layers which progressively extract more and more abstracted features. Yet it is unknown whether DCNNs match human performance at the task of view-invariant object recognition, whether they make similar errors and use similar representations for this task, and whether the answers depend on the magnitude of the viewpoint variations. To investigate these issues, we benchmarked eight state-of-the-art DCNNs, the HMAX model, and a baseline shallow model and compared their results to those of humans with backward masking. Unlike in all previous DCNN studies, we carefully controlled the magnitude of the viewpoint variations to demonstrate that shallow nets can outperform deep nets and humans when variations are weak. When facing larger variations, however, more layers were needed to match human performance and error distributions, and to have representations that are consistent with human behavior. A very deep net with 18 layers even outperformed humans at the highest variation level, using the most human-like representations.

Connected through hyperedge HE-1 (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-2 (induced by source paper "angular visual hardness"):
  with members:
    - "feature critic networks for heterogeneous domain generalization"
    - "confidence regularized self training"
    - "compressive hyperspherical energy minimization"
    - "do imagenet classifiers generalize to imagenet"
    - "decoupled networks"
    - "reducing overconfident errors outside the known distribution"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "learning towards minimum hyperspherical energy"

Connected through hyperedge HE-3 (induced by source paper "synthetic neuroscore using a neuro ai interface for evaluating generative adversarial networks"):
  with members:
    - "generative adversarial networks a survey and taxonomy"
    - "quick and easy time series generation with established image based gans"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "pros and cons of gan evaluation measures"
    - "an empirical study on evaluation metrics of generative adversarial networks"
    - "a style based generator architecture for generative adversarial networks"
    - "use of neural signals to evaluate the quality of generative adversarial network performance in facial image generation"
    - "generative image inpainting with contextual attention"

Connected through hyperedge HE-4 (induced by source paper "quantifying legibility of indoor spaces using deep convolutional neural networks case studies in train stations"):
  with members:
    - "comparing deep neural networks against humans object recognition when the signal gets weaker"
    - "r2cnn rotational region cnn for orientation robust scene text detection"
    - "deep learning the city quantifying urban perception at a global scale"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "learning deep features for discriminative localization"
    - "object detectors emerge in deep scene cnns"
    - "deep convolutional neural fields for depth estimation from a single image"

Connected through hyperedge HE-5 (induced by source paper "on learning and learned data representation by capsule networks"):
  with members:
    - "investigating capsule networks with dynamic routing for text classification"
    - "dynamic routing between capsules"
    - "practical black box attacks against machine learning"
    - "a point set generation network for 3d object reconstruction from a single image"
    - "deep residual learning for image recognition"
    - "visualizing and understanding recurrent networks"
    - "adam a method for stochastic optimization"
    - "how transferable are features in deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "crowding in humans is unlike that in convolutional neural networks"):
  with members:
    - "do deep neural networks suffer from crowding"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "empirical evaluation of rectified activations in convolutional network"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "recurrent models of visual attention"

Connected through hyperedge HE-7 (induced by source paper "distinguishing mirror from glass a big data approach to material perception"):
  with members:
    - "comparing deep neural networks against humans object recognition when the signal gets weaker"
    - "humans and deep networks largely agree on which kinds of variation make object recognition harder"
    - "instance aware semantic segmentation via multi task network cascades"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "two stream convolutional networks for action recognition in videos"

Connected through hyperedge HE-8 (induced by source paper "an analysis of deep neural networks with attention for action recognition from a neurophysiological perspective"):
  with members:
    - "attention is all we need nailing down object centric attention for egocentric activity recognition"
    - "top down attention recurrent vlad encoding for action recognition in videos"
    - "lsta long short term attention for egocentric action recognition"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "deep residual learning for image recognition"

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)