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 roots improving cnn efficiency with hierarchical filter groups"
  Abstract: We propose a new method for creating computationally efficient and compact convolutional neural networks (CNNs) using a novel sparse connection structure that resembles a tree root. This allows a significant reduction in computational cost and number of parameters compared to state-of-the-art deep CNNs, without compromising accuracy, by exploiting the sparsity of inter-layer filter dependencies. We validate our approach by using it to train more efficient variants of state-of-the-art CNN architectures, evaluated on the CIFAR10 and ILSVRC datasets. Our results show similar or higher accuracy than the baseline architectures with much less computation, as measured by CPU and GPU timings. For example, for ResNet 50, our model has 40% fewer parameters, 45% fewer floating point operations, and is 31% (12%) faster on a CPU (GPU). For the deeper ResNet 200 our model has 25% fewer floating point operations and 44% fewer parameters, while maintaining state-of-the-art accuracy. For GoogLeNet, our model has 7% fewer parameters and is 21% (16%) faster on a CPU (GPU).

Connected through hyperedge HE-1 (induced by source paper "pre defined sparsity for low complexity convolutional neural networks"):
  with members:
    - "pre defined sparse neural networks with hardware acceleration"
    - "scienet deep learning with spike assisted contextual information extraction"
    - "pconv the missing but desirable sparsity in dnn weight pruning for real time execution on mobile devices"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "admm nn an algorithm hardware co design framework of dnns using alternating direction method of multipliers"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "eyeriss v2 a flexible accelerator for emerging deep neural networks on mobile devices"
    - "rethinking the value of network pruning"

Connected through hyperedge HE-2 (induced by source paper "deep sparse band selection for hyperspectral face recognition"):
  with members:
    - "using deep cross modal hashing and error correcting codes for improving the efficiency of attribute guided facial image retrieval"
    - "attribute guided coupled gan for cross resolution face recognition"
    - "adversarial examples to fool iris recognition systems"
    - "defending against adversarial iris examples using wavelet decomposition"
    - "unsupervised image to image translation using domain specific variational information bound"
    - "unsupervised facial geometry learning for sketch to photo synthesis"
    - "object detection with deep learning a review"
    - "multibiometric secure system based on deep learning"

Connected through hyperedge HE-3 (induced by source paper "densepoint learning densely contextual representation for efficient point cloud processing"):
  with members:
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "relation shape convolutional neural network for point cloud analysis"
    - "adaptive graph convolutional neural networks"
    - "p2p net bidirectional point displacement net for shape transform"
    - "multiresolution tree networks for 3d point cloud processing"
    - "point convolutional neural networks by extension operators"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "splatnet sparse lattice networks for point cloud processing"

Connected through hyperedge HE-4 (induced by source paper "blockswap fisher guided block substitution for network compression on a budget"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "full deep neural network training on a pruned weight budget"
    - "faster gaze prediction with dense networks and fisher pruning"
    - "accelerating deep neural networks with spatial bottleneck modules"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "paraphrasing complex network network compression via factor transfer"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "neural architecture optimization"

Connected through hyperedge HE-5 (induced by source paper "fully learnable group convolution for acceleration of deep neural networks"):
  with members:
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "nisp pruning networks using neuron importance score propagation"
    - "learning transferable architectures for scalable image recognition"
    - "sphereface deep hypersphere embedding for face recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-6 (induced by source paper "all you need is a few shifts designing efficient convolutional neural networks for image classification"):
  with members:
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "a layer decomposition recomposition framework for neuron pruning towards accurate lightweight networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "constructing fast network through deconstruction of convolution"
    - "darts differentiable architecture search"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "progressive neural architecture search"

Connected through hyperedge HE-7 (induced by source paper "hetconv heterogeneous kernel based convolutions for deep cnns"):
  with members:
    - "stability based filter pruning for accelerating deep cnns"
    - "multi layer pruning framework for compressing single shot multibox detector"
    - "differential attention for visual question answering"
    - "leveraging filter correlations for deep model compression"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "nisp pruning networks using neuron importance score propagation"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-8 (induced by source paper "nasa neural articulated shape approximation"):
  with members:
    - "implicit functions in feature space for 3d shape reconstruction and completion"
    - "amass archive of motion capture as surface shapes"
    - "deferred neural rendering image synthesis using neural textures"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "cvxnets learnable convex decomposition"
    - "unsupervised part based disentangling of object shape and appearance"
    - "what do single view 3d reconstruction networks learn"
    - "cerberus a multi headed derenderer"

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)