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: "multi bias non linear activation in deep neural networks"
  Abstract: As a widely used non-linear activation, Rectified Linear Unit (ReLU) separates noise and signal in a feature map by learning a threshold or bias. However, we argue that the classification of noise and signal not only depends on the magnitude of responses, but also the context of how the feature responses would be used to detect more abstract patterns in higher layers. In order to output multiple response maps with magnitude in different ranges for a particular visual pattern, existing networks employing ReLU and its variants have to learn a large number of redundant filters. In this paper, we propose a multi-bias non-linear activation (MBA) layer to explore the information hidden in the magnitudes of responses. It is placed after the convolution layer to decouple the responses to a convolution kernel into multiple maps by multi-thresholding magnitudes, thus generating more patterns in the feature space at a low computational cost. It provides great flexibility of selecting responses to different visual patterns in different magnitude ranges to form rich representations in higher layers. Such a simple and yet effective scheme achieves the state-of-the-art performance on several benchmarks.

Connected through hyperedge HE-1 (induced by source paper "dianet dense and implicit attention network"):
  with members:
    - "towards universal object detection by domain attention"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "selective kernel networks"
    - "bam bottleneck attention module"
    - "mixed link networks"
    - "gather excite exploiting feature context in convolutional neural networks"
    - "cbam convolutional block attention module"
    - "non local neural networks"

Connected through hyperedge HE-2 (induced by source paper "multi layer pruning framework for compressing single shot multibox detector"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "towards lightweight convolutional neural networks for object detection"
    - "channel pruning for accelerating very deep neural networks"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "ssd single shot multibox detector"
    - "doubly convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "multi bin trainable linear unit for fast image restoration networks"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "soft to hard vector quantization for end to end learning compressible representations"
    - "enhanced deep residual networks for single image super resolution"
    - "learning deep cnn denoiser prior for image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "can maxout units downsize restoration networks single image super resolution using lightweight cnn with maxout units"
    - "memnet a persistent memory network for image restoration"
    - "learning convolutional networks for content weighted image compression"

Connected through hyperedge HE-4 (induced by source paper "a survey of model compression and acceleration for deep neural networks"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "detail preserving pooling in deep networks"
    - "rethinking the value of network pruning"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "learning efficient convolutional networks through network slimming"
    - "data driven sparse structure selection for deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "systematic evaluation of convolution neural network advances on the imagenet"):
  with members:
    - "an analysis of deep neural network models for practical applications"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "on architectural choices in deep learning from network structure to gradient convergence and parameter estimation"
    - "disturblabel regularizing cnn on the loss layer"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"
    - "wide residual networks"
    - "fractalnet ultra deep neural networks without residuals"

Connected through hyperedge HE-6 (induced by source paper "zoom out and in network with map attention decision for region proposal and object detection"):
  with members:
    - "learning deep features via congenerous cosine loss for person recognition"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "dssd deconvolutional single shot detector"
    - "deformable convolutional networks"
    - "recurrent scale approximation for object detection in cnn"
    - "stacked hourglass networks for human pose estimation"
    - "deepproposals hunting objects and actions by cascading deep convolutional layers"
    - "scale aware pixelwise object proposal networks"

Connected through hyperedge HE-7 (induced by source paper "zoom out and in network with recursive training for object proposal"):
  with members:
    - "stacked hourglass networks for human pose estimation"
    - "deepproposals hunting objects and actions by cascading deep convolutional layers"
    - "ssd single shot multibox detector"
    - "hypernet towards accurate region proposal generation and joint object detection"
    - "what makes for effective detection proposals"
    - "craft objects from images"
    - "object proposal evaluation protocol is gameable"
    - "learning to segment object candidates"

Connected through hyperedge HE-8 (induced by source paper "rethinking feature discrimination and polymerization for large scale recognition"):
  with members:
    - "person recognition in personal photo collections"
    - "sphereface deep hypersphere embedding for face recognition"
    - "dual deep network for visual tracking"
    - "recurrent scale approximation for object detection in cnn"
    - "zoom out and in network with map attention decision for region proposal and object detection"
    - "convolutional pose machines"
    - "faceless person recognition privacy implications in social media"
    - "learning a metric embedding for face recognition using the multibatch method"

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)