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: "emergence of complex like cells in a temporal product network with local receptive fields"
  Abstract: We introduce a new neural architecture and an unsupervised algorithm for learning invariant representations from temporal sequence of images. The system uses two groups of complex cells whose outputs are combined multiplicatively: one that represents the content of the image, constrained to be constant over several consecutive frames, and one that represents the precise location of features, which is allowed to vary over time but constrained to be sparse. The architecture uses an encoder to extract features, and a decoder to reconstruct the input from the features. The method was applied to patches extracted from consecutive movie frames and produces orientation and frequency selective units analogous to the complex cells in V1. An extension of the method is proposed to train a network composed of units with local receptive field spread over a large image of arbitrary size. A layer of complex cells, subject to sparsity constraints, pool feature units over overlapping local neighborhoods, which causes the feature units to organize themselves into pinwheel patterns of orientation-selective receptive fields, similar to those observed in the mammalian visual cortex. A feed-forward encoder efficiently computes the feature representation of full images.

Connected through hyperedge HE-1 (induced by source paper "revisiting spatial invariance with low rank local connectivity"):
  with members:
    - "condconv conditionally parameterized convolutions for efficient inference"
    - "stand alone self attention in vision models"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "attention branch network learning of attention mechanism for visual explanation"
    - "learn to pay attention"
    - "cbam convolutional block attention module"
    - "you look twice gaternet for dynamic filter selection in cnns"

Connected through hyperedge HE-2 (induced by source paper "learning spatiotemporal features of ride sourcing services with fusion convolutional network"):
  with members:
    - "deep multi view spatial temporal network for taxi demand prediction"
    - "learning spatio temporal features with 3d residual networks for action recognition"
    - "short term forecasting of passenger demand under on demand ride services a spatio temporal deep learning approach"
    - "deep spatio temporal residual networks for citywide crowd flows prediction"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "two stream convolutional networks for action recognition in videos"

Connected through hyperedge HE-3 (induced by source paper "face recognition from traditional to deep learning methods"):
  with members:
    - "representation learning by rotating your faces"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "mobilefacenets efficient cnns for accurate real time face verification on mobile devices"
    - "additive margin softmax for face verification"
    - "enhancing convolutional neural networks for face recognition with occlusion maps and batch triplet loss"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"

Connected through hyperedge HE-4 (induced by source paper "automated pruning for deep neural network compression"):
  with members:
    - "opening the black box of deep neural networks via information"
    - "eie efficient inference engine on compressed deep neural network"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "tensorflow a system for large scale machine learning"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "compressing neural networks with the hashing trick"

Connected through hyperedge HE-5 (induced by source paper "convolutional neural networks via node varying graph filters"):
  with members:
    - "geometric deep learning going beyond euclidean data"
    - "multiscale hierarchical convolutional networks"
    - "reconstruction of graph signals through percolation from seeding nodes"
    - "densely connected convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "sampling of graph signals with successive local aggregations"
    - "hierarchical overlapping clustering of network data using cut metrics"
    - "discrete signal processing on graphs sampling theory"

Connected through hyperedge HE-6 (induced by source paper "locally smoothed neural networks"):
  with members:
    - "recurrent spatial transformer networks"
    - "spatial transformer networks"
    - "ask attend and answer exploring question guided spatial attention for visual question answering"
    - "abc cnn an attention based convolutional neural network for visual question answering"
    - "draw a recurrent neural network for image generation"
    - "gen cnn a convolutional architecture for word sequence prediction"
    - "recurrent models of visual attention"
    - "multiple object recognition with visual attention"

Connected through hyperedge HE-7 (induced by source paper "convolution in convolution for network in network"):
  with members:
    - "learning sampling distributions for efficient object detection"
    - "pedestrian detection inspired by appearance constancy and shape symmetry"
    - "cascaded subpatch networks for effective cnns"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "pcanet a simple deep learning baseline for image classification"
    - "fitnets hints for thin deep nets"
    - "going deeper with convolutions"

Connected through hyperedge HE-8 (induced by source paper "on the exploration of convolutional fusion networks for visual recognition"):
  with members:
    - "holistically nested edge detection"
    - "deep learning with s shaped rectified linear activation units"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "exploiting local features from deep networks for image retrieval"
    - "multi scale recognition with dag cnns"
    - "aggregating deep convolutional features for image retrieval"
    - "analyzing the performance of multilayer neural networks for object 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.NE(Neural and Evolutionary Computing)