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: "sep nets small and effective pattern networks"
  Abstract: While going deeper has been witnessed to improve the performance of convolutional neural networks (CNN), going smaller for CNN has received increasing attention recently due to its attractiveness for mobile/embedded applications. It remains an active and important topic how to design a small network while retaining the performance of large and deep CNNs (e.g., Inception Nets, ResNets). Albeit there are already intensive studies on compressing the size of CNNs, the considerable drop of performance is still a key concern in many designs. This paper addresses this concern with several new contributions. First, we propose a simple yet powerful method for compressing the size of deep CNNs based on parameter binarization. The striking difference from most previous work on parameter binarization/quantization lies at different treatments of $1\times 1$ convolutions and $k\times k$ convolutions ($k>1$), where we only binarize $k\times k$ convolutions into binary patterns. The resulting networks are referred to as pattern networks. By doing this, we show that previous deep CNNs such as GoogLeNet and Inception-type Nets can be compressed dramatically with marginal drop in performance. Second, in light of the different functionalities of $1\times 1$ (data projection/transformation) and $k\times k$ convolutions (pattern extraction), we propose a new block structure codenamed the pattern residual block that adds transformed feature maps generated by $1\times 1$ convolutions to the pattern feature maps generated by $k\times k$ convolutions, based on which we design a small network with $\sim 1$ million parameters. Combining with our parameter binarization, we achieve better performance on ImageNet than using similar sized networks including recently released Google MobileNets.

Connected through hyperedge HE-1 (induced by source paper "mdinference balancing inference accuracy andlatency for mobile applications"):
  with members:
    - "benchmark analysis of representative deep neural network architectures"
    - "learning transferable architectures for scalable image recognition"
    - "branchynet fast inference via early exiting from deep neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "distributed deep neural networks over the cloud the edge and end devices"
    - "cloud based or on device an empirical study of mobile deep inference"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "clipper a low latency online prediction serving system"

Connected through hyperedge HE-2 (induced by source paper "mpdcompress matrix permutation decomposition algorithm for deep neural network compression"):
  with members:
    - "shakeout a new approach to regularized deep neural network training"
    - "on the importance of stereo for accurate depth estimation an efficient semi supervised deep neural network approach"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "deeppicar a low cost deep neural network based autonomous car"
    - "structured deep neural network pruning via matrix pivoting"
    - "ese efficient speech recognition engine with sparse lstm on fpga"

Connected through hyperedge HE-3 (induced by source paper "binaryrelax a relaxation approach for training deep neural networks with quantized weights"):
  with members:
    - "stochastic layer wise precision in deep neural networks"
    - "extremely low bit neural network squeeze the last bit out with admm"
    - "training quantized nets a deeper understanding"
    - "model compression as constrained optimization with application to neural nets part ii quantization"
    - "model compression as constrained optimization with application to neural nets part i general framework"
    - "deep learning with low precision by half wave gaussian quantization"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "deep relaxation partial differential equations for optimizing deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "a survey of mobile computing for the visually impaired"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "vision and language navigation interpreting visually grounded navigation instructions in real environments"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "communication efficient learning of deep networks from decentralized data"
    - "trained ternary quantization"

Connected through hyperedge HE-5 (induced by source paper "blockdrop dynamic inference paths in residual networks"):
  with members:
    - "nisp pruning networks using neuron importance score propagation"
    - "data driven sparse structure selection for deep neural networks"
    - "dynamic deep neural networks optimizing accuracy efficiency trade offs by selective execution"
    - "training quantized nets a deeper understanding"
    - "branchynet fast inference via early exiting from deep neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "multi scale dense networks for resource efficient image classification"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-6 (induced by source paper "ternary residual networks"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "sharp minima can generalize for deep nets"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "finn a framework for fast scalable binarized neural network inference"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "learning structured sparsity in deep neural networks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"

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)