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: "dnn feature map compression using learned representation over gf 2"
  Abstract: In this paper, we introduce a method to compress intermediate feature maps of deep neural networks (DNNs) to decrease memory storage and bandwidth requirements during inference. Unlike previous works, the proposed method is based on converting fixed-point activations into vectors over the smallest GF(2) finite field followed by nonlinear dimensionality reduction (NDR) layers embedded into a DNN. Such an end-to-end learned representation finds more compact feature maps by exploiting quantization redundancies within the fixed-point activations along the channel or spatial dimensions. We apply the proposed network architectures derived from modified SqueezeNet and MobileNetV2 to the tasks of ImageNet classification and PASCAL VOC object detection. Compared to prior approaches, the conducted experiments show a factor of 2 decrease in memory requirements with minor degradation in accuracy while adding only bitwise computations.

Connected through hyperedge HE-1 (induced by source paper "ebpc extended bit plane compression for deep neural network inference and training accelerators"):
  with members:
    - "and the bit goes down revisiting the quantization of neural networks"
    - "additive noise annealing and approximation properties of quantized neural networks"
    - "a 64 mw dnn based visual navigation engine for autonomous nano drones"
    - "rpr random partition relaxation for training binary and ternary weight neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "yolov3 an incremental improvement"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "multiposenet fast multi person pose estimation using pose residual network"

Connected through hyperedge HE-2 (induced by source paper "cat compression aware training for bandwidth reduction"):
  with members:
    - "ebpc extended bit plane compression for deep neural network inference and training accelerators"
    - "compressibility loss for neural network weights"
    - "feature map transform coding for energy efficient cnn inference"
    - "quantization networks"
    - "lutnet rethinking inference in fpga soft logic"
    - "sparse networks from scratch faster training without losing performance"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "efficient and effective quantization for sparse dnns"

Connected through hyperedge HE-3 (induced by source paper "feature map transform coding for energy efficient cnn inference"):
  with members:
    - "dnnvm end to end compiler leveraging heterogeneous optimizations on fpga based cnn accelerators"
    - "regularizing activation distribution for training binarized deep networks"
    - "lutnet rethinking inference in fpga soft logic"
    - "low bit quantization of neural networks for efficient inference"
    - "same same but different recovering neural network quantization error through weight factorization"
    - "swalp stochastic weight averaging in low precision training"
    - "hawq hessian aware quantization of neural networks with mixed precision"
    - "faster gaze prediction with dense networks and fisher pruning"

Connected through hyperedge HE-4 (induced by source paper "accelerating convolutional neural networks via activation map compression"):
  with members:
    - "stochastic activation pruning for robust adversarial defense"
    - "multilayer convolutional sparse modeling pursuit and dictionary learning"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "weightless lossy weight encoding for deep neural network compression"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"
    - "deep learning with low precision by half wave gaussian quantization"

Connected through hyperedge HE-5 (induced by source paper "extended bit plane compression for convolutional neural network accelerators"):
  with members:
    - "cbinfer exploiting frame to frame locality for faster convolutional network inference on video streams"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "nullhop a flexible convolutional neural network accelerator based on sparse representations of feature maps"
    - "a survey on deep learning in medical image analysis"
    - "compressing dma engine leveraging activation sparsity for training deep neural networks"
    - "origami a 803 gop s w convolutional network accelerator"
    - "eie efficient inference engine on compressed deep neural network"

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)