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: "sharesnet reducing residual network parameter number by sharing weights"
  Abstract: Deep Residual Networks have reached the state of the art in many image processing tasks such image classification. However, the cost for a gain in accuracy in terms of depth and memory is prohibitive as it requires a higher number of residual blocks, up to double the initial value. To tackle this problem, we propose in this paper a way to reduce the redundant information of the networks. We share the weights of convolutional layers between residual blocks operating at the same spatial scale. The signal flows multiple times in the same convolutional layer. The resulting architecture, called ShaResNet, contains block specific layers and shared layers. These ShaResNet are trained exactly in the same fashion as the commonly used residual networks. We show, on the one hand, that they are almost as efficient as their sequential counterparts while involving less parameters, and on the other hand that they are more efficient than a residual network with the same number of parameters. For example, a 152-layer-deep residual network can be reduced to 106 convolutional layers, i.e. a parameter gain of 39\%, while loosing less than 0.2\% accuracy on ImageNet.

Connected through hyperedge HE-1 (induced by source paper "extd extremely tiny face detector via iterative filter reuse"):
  with members:
    - "depthwise convolution is all you need for learning multiple visual domains"
    - "thundernet towards real time generic object detection"
    - "dfanet deep feature aggregation for real time semantic segmentation"
    - "improved selective refinement network for face detection"
    - "fishnet a versatile backbone for image region and pixel level prediction"
    - "dsfd dual shot face detector"
    - "tiny dsod lightweight object detection for resource restricted usages"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-2 (induced by source paper "deep anchored convolutional neural networks"):
  with members:
    - "learning to prune filters in convolutional neural networks"
    - "learning multiple visual domains with residual adapters"
    - "dual path networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "deep networks with stochastic depth"
    - "eie efficient inference engine on compressed deep neural network"
    - "wide residual networks"
    - "pruning filters for efficient convnets"

Connected through hyperedge HE-3 (induced by source paper "exploring weight symmetry in deep neural networks"):
  with members:
    - "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"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "sequence level knowledge distillation"
    - "aggregated residual transformations for deep neural networks"
    - "tying word vectors and word classifiers a loss framework for language modeling"

Connected through hyperedge HE-4 (induced by source paper "recurrent knowledge distillation"):
  with members:
    - "mask r cnn"
    - "residual networks are exponential ensembles of relatively shallow networks"
    - "resfeats residual network based features for image classification"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "sketching and neural networks"
    - "highway and residual networks learn unrolled iterative estimation"
    - "feature pyramid networks for object detection"
    - "wide residual networks"

Connected through hyperedge HE-5 (induced by source paper "iamnn iterative and adaptive mobile neural network for efficient image classification"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "residual connections encourage iterative inference"
    - "adaptive computation time for recurrent neural networks"
    - "residual networks behave like ensembles of relatively shallow networks"
    - "highway and residual networks learn unrolled iterative estimation"
    - "spatially adaptive computation time for 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)