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: "deep feature compression for collaborative object detection"
  Abstract: Recent studies have shown that the efficiency of deep neural networks in mobile applications can be significantly improved by distributing the computational workload between the mobile device and the cloud. This paradigm, termed collaborative intelligence, involves communicating feature data between the mobile and the cloud. The efficiency of such approach can be further improved by lossy compression of feature data, which has not been examined to date. In this work we focus on collaborative object detection and study the impact of both near-lossless and lossy compression of feature data on its accuracy. We also propose a strategy for improving the accuracy under lossy feature compression. Experiments indicate that using this strategy, the communication overhead can be reduced by up to 70% without sacrificing accuracy.

Connected through hyperedge HE-1 (induced by source paper "run time deep model multiplexing"):
  with members:
    - "bottlenet a deep learning architecture for intelligent mobile cloud computing services"
    - "towards collaborative intelligence friendly architectures for deep learning"
    - "jointdnn an efficient training and inference engine for intelligent mobile cloud computing services"
    - "mnasnet platform aware neural architecture search for mobile"
    - "an analysis of deep neural network models for practical applications"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "distributed deep neural networks over the cloud the edge and end devices"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-2 (induced by source paper "collaborative inference for efficient remote monitoring"):
  with members:
    - "deep learning for time series classification a review"
    - "efficient inference on deep neural networks by dynamic representations and decision gates"
    - "doubly nested network for resource efficient inference"
    - "cardiologist level arrhythmia detection with convolutional neural networks"
    - "deep learning with convolutional neural networks for eeg decoding and visualization"
    - "xgboost a scalable tree boosting system"
    - "doubly convolutional neural networks"
    - "group equivariant convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "bit allocation for multi task collaborative intelligence"):
  with members:
    - "multi task learning with compressible features for collaborative intelligence"
    - "bottlenet a deep learning architecture for intelligent mobile cloud computing services"
    - "towards collaborative intelligence friendly architectures for deep learning"
    - "yolov3 an incremental improvement"
    - "ai benchmark running deep neural networks on android smartphones"
    - "multi task learning as multi objective optimization"
    - "intermediate deep feature compression the next battlefield of intelligent sensing"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-4 (induced by source paper "back and forth prediction for deep tensor compression"):
  with members:
    - "bottlenet a deep learning architecture for intelligent mobile cloud computing services"
    - "towards collaborative intelligence friendly architectures for deep learning"
    - "jointdnn an efficient training and inference engine for intelligent mobile cloud computing services"
    - "yolov3 an incremental improvement"
    - "yolo9000 better faster stronger"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-5 (induced by source paper "shared mobile cloud inference for collaborative intelligence"):
  with members:
    - "yolov3 an incremental improvement"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "multi task learning with compressible features for collaborative intelligence"):
  with members:
    - "ai benchmark running deep neural networks on android smartphones"
    - "conditional probability models for deep image compression"
    - "intermediate deep feature compression the next battlefield of intelligent sensing"
    - "learning convolutional networks for content weighted image compression"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "conditional image generation with pixelcnn decoders"
    - "pixel recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "bottlenet an end to end approach for feature compression in device edge co inference systems"):
  with members:
    - "improving device edge cooperative inference of deep learning via 2 step pruning"
    - "bottlenet a deep learning architecture for intelligent mobile cloud computing services"
    - "jointdnn an efficient training and inference engine for intelligent mobile cloud computing services"
    - "neural joint source channel coding"
    - "jalad joint accuracy and latency aware deep structure decoupling for edge cloud execution"
    - "edge host partitioning of deep neural networks with feature space encoding for resource constrained internet of things platforms"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "bottlenet a deep learning architecture for intelligent mobile cloud computing services"):
  with members:
    - "towards collaborative intelligence friendly architectures for deep learning"
    - "jointdnn an efficient training and inference engine for intelligent mobile cloud computing services"
    - "intermediate deep feature compression the next battlefield of intelligent sensing"
    - "matching networks for one shot learning"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "cudnn efficient primitives for deep learning"

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)