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: "intermediate deep feature compression the next battlefield of intelligent sensing"
  Abstract: The recent advances of hardware technology have made the intelligent analysis equipped at the front-end with deep learning more prevailing and practical. To better enable the intelligent sensing at the front-end, instead of compressing and transmitting visual signals or the ultimately utilized top-layer deep learning features, we propose to compactly represent and convey the intermediate-layer deep learning features of high generalization capability, to facilitate the collaborating approach between front and cloud ends. This strategy enables a good balance among the computational load, transmission load and the generalization ability for cloud servers when deploying the deep neural networks for large scale cloud based visual analysis. Moreover, the presented strategy also makes the standardization of deep feature coding more feasible and promising, as a series of tasks can simultaneously benefit from the transmitted intermediate layers. We also present the results for evaluation of lossless deep feature compression with four benchmark data compression methods, which provides meaningful investigations and baselines for future research and standardization activities.

Connected through hyperedge HE-1 (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"
    - "deep feature compression for collaborative object detection"
    - "ai benchmark running deep neural networks on android smartphones"
    - "multi task learning as multi objective optimization"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-2 (induced by source paper "multi task learning with compressible features for collaborative intelligence"):
  with members:
    - "deep feature compression for collaborative object detection"
    - "ai benchmark running deep neural networks on android smartphones"
    - "conditional probability models for deep image compression"
    - "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-3 (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"
    - "deep feature compression for collaborative object detection"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "towards collaborative intelligence friendly architectures for deep learning"):
  with members:
    - "jointdnn an efficient training and inference engine for intelligent mobile cloud computing services"
    - "deep feature compression for collaborative object detection"
    - "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"
    - "cudnn efficient primitives for deep learning"
    - "distributed representations of words and phrases and their compositionality"

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.MM(Multimedia)