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: "compact descriptors for video analysis the emerging mpeg standard"
  Abstract: This paper provides an overview of the on-going compact descriptors for video analysis standard (CDVA) from the ISO/IEC moving pictures experts group (MPEG). MPEG-CDVA targets at defining a standardized bitstream syntax to enable interoperability in the context of video analysis applications. During the developments of MPEGCDVA, a series of techniques aiming to reduce the descriptor size and improve the video representation ability have been proposed. This article describes the new standard that is being developed and reports the performance of these key technical contributions.

Connected through hyperedge HE-1 (induced by source paper "2 bit model compression of deep convolutional neural network on asic engine for image retrieval"):
  with members:
    - "compression of deep neural networks for image instance retrieval"
    - "a survey of model compression and acceleration for deep neural networks"
    - "deep image retrieval learning global representations for image search"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "learning both weights and connections for efficient neural networks"
    - "compressing deep convolutional networks using vector quantization"

Connected through hyperedge HE-2 (induced by source paper "intermediate deep feature compression the next battlefield of intelligent sensing"):
  with members:
    - "yolov3 an incremental improvement"
    - "deep feature compression for collaborative object detection"
    - "effective multi query expansions collaborative deep networks for robust landmark retrieval"
    - "stack captioning coarse to fine learning for image captioning"
    - "a survey of fpga based neural network accelerator"
    - "ai oriented large scale video management for smart city technologies standards and beyond"
    - "deep image retrieval learning global representations for image search"
    - "end to end learning for self driving cars"

Connected through hyperedge HE-3 (induced by source paper "vision and challenges for knowledge centric networking kcn"):
  with members:
    - "deep learning for sensor based activity recognition a survey"
    - "knowledge defined networking"

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)