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 "req yolo a resource aware efficient quantization framework for object detection on fpgas"):
  Abstract: Deep neural networks (DNNs), as the basis of object detection, will play a key role in the development of future autonomous systems with full autonomy. The autonomous systems have special requirements of real-time, energy-efficient implementations of DNNs on a power-constrained system. Two research thrusts are dedicated to performance and energy efficiency enhancement of the inference phase of DNNs. The first one is model compression techniques while the second is efficient hardware implementation. Recent works on extremely-low-bit CNNs such as the binary neural network (BNN) and XNOR-Net replace the traditional floating-point operations with binary bit operations which significantly reduces the memory bandwidth and storage requirement. However, it suffers from non-negligible accuracy loss and underutilized digital signal processing (DSP) blocks of FPGAs. To overcome these limitations, this paper proposes REQ-YOLO, a resource-aware, systematic weight quantization framework for object detection, considering both algorithm and hardware resource aspects in object detection. We adopt the block-circulant matrix method and propose a heterogeneous weight quantization using the Alternating Direction Method of Multipliers (ADMM), an effective optimization technique for general, non-convex optimization problems. To achieve real-time, highly-efficient implementations on FPGA, we present the detailed hardware implementation of block circulant matrices on CONV layers and develop an efficient processing element (PE) structure supporting the heterogeneous weight quantization, CONV dataflow and pipelining techniques, design optimization, and a template-based automatic synthesis framework to optimally exploit hardware resource. Experimental results show that our proposed REQ-YOLO framework can significantly compress the YOLO model while introducing very small accuracy degradation.

Includes members:
  Member "yolov3 an incremental improvement":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "c lstm enabling efficient lstm using structured compression techniques on fpgas":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "elsa a throughput optimized design of an lstm accelerator for energy constrained devices")
  Member "batch renormalization towards reducing minibatch dependence in batch normalized models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rethinking batch normalization in transformers")
  Member "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "π nets deep polynomial neural networks")
  Member "finn a framework for fast scalable binarized neural network inference":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sqwa stochastic quantized weight averaging for improving the generalization capability of low precision deep neural networks")
  Member "theoretical properties for neural networks with weight matrices of low displacement rank":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "circconv a structured convolution with low complexity")
  Member "ese efficient speech recognition engine with sparse lstm on fpga":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cluster pruning an efficient filter pruning method for edge ai vision applications")
  Member "a unified multi scale deep convolutional neural network for fast object detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "ha ccn hierarchical attention based crowd counting network")

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)