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: "performance guaranteed network acceleration via high order residual quantization"
  Abstract: Input binarization has shown to be an effective way for network acceleration. However, previous binarization scheme could be regarded as simple pixel-wise thresholding operations (i.e., order-one approximation) and suffers a big accuracy loss. In this paper, we propose a highorder binarization scheme, which achieves more accurate approximation while still possesses the advantage of binary operation. In particular, the proposed scheme recursively performs residual quantization and yields a series of binary input images with decreasing magnitude scales. Accordingly, we propose high-order binary filtering and gradient propagation operations for both forward and backward computations. Theoretical analysis shows approximation error guarantee property of proposed method. Extensive experimental results demonstrate that the proposed scheme yields great recognition accuracy while being accelerated.

Connected through hyperedge HE-1 (induced by source paper "forward and backward information retention for accurate binary neural networks"):
  with members:
    - "circulant binary convolutional networks enhancing the performance of 1 bit dcnns with circulant back propagation"
    - "improved training of binary networks for human pose estimation and image recognition"
    - "libra r cnn towards balanced learning for object detection"
    - "bayesian optimized 1 bit cnns"
    - "regularizing activation distribution for training binarized deep networks"
    - "dynamic curriculum learning for imbalanced data classification"
    - "xnor net improved binary neural networks"
    - "differentiable soft quantization bridging full precision and low bit neural networks"

Connected through hyperedge HE-2 (induced by source paper "automatic pruning for quantized neural networks"):
  with members:
    - "amc automl for model compression and acceleration on mobile devices"
    - "proxquant quantized neural networks via proximal operators"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "learning to quantize deep networks by optimizing quantization intervals with task loss"
    - "discrimination aware channel pruning for deep neural networks"
    - "relaxed quantization for discretized neural networks"
    - "snip single shot network pruning based on connection sensitivity"
    - "skipnet learning dynamic routing in convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "effective training of convolutional neural networks with low bitwidth weights and activations"):
  with members:
    - "knowledge adaptation for efficient semantic segmentation"
    - "single path one shot neural architecture search with uniform sampling"
    - "precision highway for ultra low precision quantization"
    - "amc automl for model compression and acceleration on mobile devices"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "learning to quantize deep networks by optimizing quantization intervals with task loss"
    - "proxylessnas direct neural architecture search on target task and hardware"

Connected through hyperedge HE-4 (induced by source paper "training quantized neural networks with the full precision auxiliary module"):
  with members:
    - "effective training of convolutional neural networks with low bitwidth weights and activations"
    - "circulant binary convolutional networks enhancing the performance of 1 bit dcnns with circulant back propagation"
    - "relational knowledge distillation"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "yolov3 an incremental improvement"
    - "proxquant quantized neural networks via proximal operators"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "self supervised knowledge distillation using singular value decomposition"

Connected through hyperedge HE-5 (induced by source paper "training quantized network with auxiliary gradient module"):
  with members:
    - "precision highway for ultra low precision quantization"
    - "slimmable neural networks"
    - "model compression via distillation and quantization"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "proxquant quantized neural networks via proximal operators"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "learning to quantize deep networks by optimizing quantization intervals with task loss"
    - "relaxed quantization for discretized neural networks"

Connected through hyperedge HE-6 (induced by source paper "rpr random partition relaxation for training binary and ternary weight neural networks"):
  with members:
    - "ebpc extended bit plane compression for deep neural network inference and training accelerators"
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "quantization networks"
    - "fann on mcu an open source toolkit for energy efficient neural network inference at the edge of the internet of things"
    - "hawq hessian aware quantization of neural networks with mixed precision"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "fbnet hardware aware efficient convnet design via differentiable neural architecture search"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "ir net forward and backward information retention for highly accurate binary neural networks"):
  with members:
    - "libra r cnn towards balanced learning for object detection"
    - "regularizing activation distribution for training binarized deep networks"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "dabnn a super fast inference framework for binary neural networks on arm devices"
    - "from hashing to cnns training binaryweight networks via hashing"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "simultaneously optimizing weight and quantizer of ternary neural network using truncated gaussian approximation"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "low bit quantization of neural networks for efficient inference"):
  with members:
    - "model compression via distillation and quantization"
    - "syq learning symmetric quantization for efficient deep neural networks"
    - "post training 4 bit quantization of convolution networks for rapid deployment"
    - "in datacenter performance analysis of a tensor processing unit"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "towards accurate binary convolutional neural network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "learning discrete weights using the local reparameterization trick"

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)