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: "quantization of fully convolutional networks for accurate biomedical image segmentation"
  Abstract: With pervasive applications of medical imaging in health-care, biomedical image segmentation plays a central role in quantitative analysis, clinical diagno- sis, and medical intervention. Since manual anno- tation su ers limited reproducibility, arduous e orts, and excessive time, automatic segmentation is desired to process increasingly larger scale histopathological data. Recently, deep neural networks (DNNs), par- ticularly fully convolutional networks (FCNs), have been widely applied to biomedical image segmenta- tion, attaining much improved performance. At the same time, quantization of DNNs has become an ac- tive research topic, which aims to represent weights with less memory (precision) to considerably reduce memory and computation requirements of DNNs while maintaining acceptable accuracy. In this paper, we apply quantization techniques to FCNs for accurate biomedical image segmentation. Unlike existing litera- ture on quantization which primarily targets memory and computation complexity reduction, we apply quan- tization as a method to reduce over tting in FCNs for better accuracy. Speci cally, we focus on a state-of- the-art segmentation framework, suggestive annotation [22], which judiciously extracts representative annota- tion samples from the original training dataset, obtain- ing an e ective small-sized balanced training dataset. We develop two new quantization processes for this framework: (1) suggestive annotation with quantiza- tion for highly representative training samples, and (2) network training with quantization for high accuracy. Extensive experiments on the MICCAI Gland dataset show that both quantization processes can improve the segmentation performance, and our proposed method exceeds the current state-of-the-art performance by up to 1%. In addition, our method has a reduction of up to 6.4x on memory usage.

Connected through hyperedge HE-1 (induced by source paper "device circuit architecture co exploration for computing in memory neural accelerators"):
  with members:
    - "video action recognition via neural architecture searching"
    - "hardware software co exploration of neural architectures"
    - "accuracy vs efficiency achieving both through fpga implementation aware neural architecture search"
    - "achieving super linear speedup across multi fpga for real time dnn inference"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "on neural architecture search for resource constrained hardware platforms"
    - "architecture search of dynamic cells for semantic video segmentation"
    - "fbnet hardware aware efficient convnet design via differentiable neural architecture search"

Connected through hyperedge HE-2 (induced by source paper "scnn a general distribution based statistical convolutional neural network with application to video object detection"):
  with members:
    - "dac sdc low power object detection challenge for uav applications"
    - "yolov3 an incremental improvement"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "flow guided feature aggregation for video object detection"
    - "densely connected convolutional networks"
    - "seq nms for video object detection"
    - "unitbox an advanced object detection network"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "exploiting computation power of blockchain for biomedical image segmentation"):
  with members:
    - "energy recycling blockchain with proof of deep learning"
    - "scnn a general distribution based statistical convolutional neural network with application to video object detection"
    - "blockchain technology overview"
    - "nnu net self adapting framework for u net based medical image segmentation"
    - "ct realistic lung nodule simulation from 3d conditional generative adversarial networks for robust lung segmentation"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "achieving super linear speedup across multi fpga for real time dnn inference"):
  with members:
    - "hardware software co exploration of neural architectures"
    - "accuracy vs efficiency achieving both through fpga implementation aware neural architecture search"
    - "on the universal approximability and complexity bounds of quantized relu neural networks"
    - "an unsupervised learning model for deformable medical image registration"
    - "in datacenter performance analysis of a tensor processing unit"
    - "recent trends in deep learning based natural language processing"
    - "maximizing cnn accelerator efficiency through resource partitioning"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-5 (induced by source paper "real time boiler control optimization with machine learning"):
  with members:
    - "pbgen partial binarization of deconvolution based generators for edge intelligence"
    - "on the universal approximability and complexity bounds of quantized relu neural networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "sequential click prediction for sponsored search with recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "privacy aware cost effective scheduling considering non schedulable appliances in smart home"):
  with members:
    - "increasing smart meter privacy through energy harvesting and storage devices"

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)