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: "fixed point convolutional neural network for real time video processing in fpga"
  Abstract: Modern mobile neural networks with a reduced number of weights and parameters do a good job with image classification tasks, but even they may be too complex to be implemented in an FPGA for video processing tasks. The article proposes neural network architecture for the practical task of recognizing images from a camera, which has several advantages in terms of speed. This is achieved by reducing the number of weights, moving from a floating-point to a fixed-point arithmetic, and due to a number of hardware-level optimizations associated with storing weights in blocks, a shift register, and an adjustable number of convolutional blocks that work in parallel. The article also proposed methods for adapting the existing data set for solving a different task. As the experiments showed, the proposed neural network copes well with real-time video processing even on the cheap FPGAs.

Connected through hyperedge HE-1 (induced by source paper "lightweight convolutional representations for on device natural language processing"):
  with members:
    - "are sixteen heads really better than one"
    - "the state of sparsity in deep neural networks"
    - "accelerator aware pruning for convolutional neural networks"
    - "rethinking the value of network pruning"
    - "convolutional sequence to sequence learning"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "language modeling with gated convolutional networks"
    - "gaussian error linear units gelus"

Connected through hyperedge HE-2 (induced by source paper "deep joint source channel coding for wireless image transmission"):
  with members:
    - "an autoencoder based learned image compressor description of challenge proposal by nctu"
    - "deep convolutional autoencoder based lossy image compression"
    - "deep learning methods for improved decoding of linear codes"
    - "ai benchmark running deep neural networks on android smartphones"
    - "ofdm autoencoder for end to end learning of communications systems"
    - "deep learning for joint source channel coding of text"
    - "an introduction to deep learning for the physical layer"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-3 (induced by source paper "automatic instrument segmentation in robot assisted surgery using deep learning"):
  with members:
    - "albumentations fast and flexible image augmentations"
    - "deep convolutional neural networks for breast cancer histology image analysis"
    - "paediatric bone age assessment using deep convolutional neural networks"
    - "angiodysplasia detection and localization using deep convolutional neural networks"
    - "ternausnetv2 fully convolutional network for instance segmentation"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "automatic knee osteoarthritis diagnosis from plain radiographs a deep learning based approach"
    - "linknet exploiting encoder representations for efficient semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "angiodysplasia detection and localization using deep convolutional neural networks"):
  with members:
    - "albumentations fast and flexible image augmentations"
    - "deep convolutional neural networks for breast cancer histology image analysis"
    - "paediatric bone age assessment using deep convolutional neural networks"
    - "automatic instrument segmentation in robot assisted surgery using deep learning"
    - "ternausnetv2 fully convolutional network for instance segmentation"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "automatic knee osteoarthritis diagnosis from plain radiographs a deep learning based approach"
    - "linknet exploiting encoder representations for efficient semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "deep learning approaches for understanding simple speech commands"):
  with members:
    - "deep convolutional neural networks for breast cancer histology image analysis"
    - "paediatric bone age assessment using deep convolutional neural networks"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "automatic instrument segmentation in robot assisted surgery using deep learning"
    - "wavenet a generative model for raw audio"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "deep residual learning for image recognition"

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)