human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "efficient hardware implementation of incremental learning and inference on chip"):
  Abstract: In this paper, we tackle the problem of incrementally learning a classifier, one example at a time, directly on chip. To this end, we propose an efficient hardware implementation of a recently introduced incremental learning procedure that achieves state-of-the-art performance by combining transfer learning with majority votes and quantization techniques. The proposed design is able to accommodate for both new examples and new classes directly on the chip. We detail the hardware implementation of the method (implemented on FPGA target) and show it requires limited resources while providing a significant acceleration compared to using a CPU.

Includes members:
  Member "learning to prune filters in convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sasl saliency adaptive sparsity learning for neural network acceleration")
  Member "constructing fast network through deconstruction of convolution":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards the systematic reporting of the energy and carbon footprints of machine learning")
  Member "transfer incremental learning using data augmentation":
    also connected through other hyperedges:
      (none)
  Member "shift a zero flop zero parameter alternative to spatial convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards efficient model compression via learned global ranking")
  Member "towards the limit of network quantization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "trends and advancements in deep neural network communication")
  Member "icarl incremental classifier and representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "deep learning on fpgas past present and future":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "poet bin power efficient tiny binary neurons")
  Member "binaryconnect training deep neural networks with binary weights during propagations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sparse weight activation training")

Question: Please tell me which class the source paper 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)