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: "rigid motion scattering for texture classification"
  Abstract: A rigid-motion scattering computes adaptive invariants along translations and rotations, with a deep convolutional network. Convolutions are calculated on the rigid-motion group, with wavelets defined on the translation and rotation variables. It preserves joint rotation and translation information, while providing global invariants at any desired scale. Texture classification is studied, through the characterization of stationary processes from a single realization. State-of-the-art results are obtained on multiple texture data bases, with important rotation and scaling variabilities.

Connected through hyperedge HE-1 (induced by source paper "general e 2 equivariant steerable cnns"):
  with members:
    - "spherical cnns on unstructured grids"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "learning to convolve a generalized weight tying approach"
    - "n body networks a covariant hierarchical neural network architecture for learning atomic potentials"
    - "land cover mapping at very high resolution with rotation equivariant cnns towards small yet accurate models"
    - "roto translation covariant convolutional networks for medical image analysis"
    - "multi directional geodesic neural networks via equivariant convolution"
    - "3d g cnns for pulmonary nodule detection"

Connected through hyperedge HE-2 (induced by source paper "in defense of pre trained imagenet architectures for real time semantic segmentation of road driving images"):
  with members:
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "guided upsampling network for real time semantic segmentation"
    - "light weight refinenet for real time semantic segmentation"
    - "efficient semantic segmentation using gradual grouping"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "learning transferable architectures for scalable image recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "linknet exploiting encoder representations for efficient semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "machine learning at the network edge a survey"):
  with members:
    - "energy efficient radio resource allocation for federated edge learning"
    - "network offloading policies for cloud robotics a learning based approach"
    - "deep learning at the edge"
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "edgelens deep learning based object detection in integrated iot fog and cloud computing environments"
    - "dabnn a super fast inference framework for binary neural networks on arm devices"
    - "pcamp performance comparison of machine learning packages on the edges"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"

Connected through hyperedge HE-4 (induced by source paper "ic network an inter layer collision network for image classification"):
  with members:
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "darts differentiable architecture search"
    - "mnasnet platform aware neural architecture search for mobile"
    - "learning transferable architectures for scalable image recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeeze and excitation networks"
    - "dual path networks"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "squeezed very deep convolutional neural networks for text classification"):
  with members:
    - "attention is all you need"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "do convolutional networks need to be deep for text classification"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "depthwise separable convolutions for neural machine translation"
    - "very deep convolutional networks for text classification"
    - "densely connected convolutional networks"
    - "xception deep learning with depthwise separable convolutions"

Connected through hyperedge HE-6 (induced by source paper "ensyth a pruning approach to synthesis of deep learning ensembles"):
  with members:
    - "fast convex pruning of deep neural networks"
    - "a novel channel pruning method for deep neural network compression"
    - "deep mutual learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "channel pruning for accelerating very deep neural networks"
    - "xception deep learning with depthwise separable convolutions"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-7 (induced by source paper "deep demosaicing for edge implementation"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "efficient neural architecture search via parameter sharing"
    - "learning deep convolutional networks for demosaicing"
    - "learning transferable architectures for scalable image recognition"
    - "sgdr stochastic gradient descent with restarts"
    - "deep learning with darwin evolutionary synthesis of deep neural networks"
    - "eie efficient inference engine on compressed deep neural network"

Connected through hyperedge HE-8 (induced by source paper "fast accurate and lightweight super resolution with cascading residual network"):
  with members:
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "enhanced deep residual networks for single image super resolution"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "multi level and multi scale feature aggregation using pretrained convolutional neural networks for music auto tagging"
    - "memnet a persistent memory network for image restoration"
    - "accelerating the super resolution convolutional neural network"
    - "identity mappings in deep residual 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.CV(Computer Vision and Pattern Recognition)