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: "circulant binary convolutional networks enhancing the performance of 1 bit dcnns with circulant back propagation"
  Abstract: The rapidly decreasing computation and memory cost has recently driven the success of many applications in the field of deep learning. Practical applications of deep learning in resource-limited hardware, such as embedded devices and smart phones, however, remain challenging. For binary convolutional networks, the reason lies in the degraded representation caused by binarizing full-precision filters. To address this problem, we propose new circulant filters (CiFs) and a circulant binary convolution (CBConv) to enhance the capacity of binarized convolutional features via our circulant back propagation (CBP). The CiFs can be easily incorporated into existing deep convolutional neural networks (DCNNs), which leads to new Circulant Binary Convolutional Networks (CBCNs). Extensive experiments confirm that the performance gap between the 1-bit and full-precision DCNNs is minimized by increasing the filter diversity, which further increases the representational ability in our networks. Our experiments on ImageNet show that CBCNs achieve 61.4% top-1 accuracy with ResNet18. Compared to the state-of-the-art such as XNOR, CBCNs can achieve up to 10% higher top-1 accuracy with more powerful representational ability.

Connected through hyperedge HE-1 (induced by source paper "learning architectures for binary networks"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "binarized neural architecture search"
    - "mobinet a mobile binary network for image classification"
    - "bayesian optimized 1 bit cnns"
    - "searching for a robust neural architecture in four gpu hours"
    - "exploring randomly wired neural networks for image recognition"
    - "xnor net improved binary neural networks"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"

Connected through hyperedge HE-2 (induced by source paper "bats binary architecture search"):
  with members:
    - "learning architectures for binary networks"
    - "back to simplicity how to train accurate bnns from scratch"
    - "regularizing activation distribution for training binarized deep networks"
    - "accurate and compact convolutional neural networks with trained binarization"
    - "xnor net improved binary neural networks"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "latent weights do not exist rethinking binarized neural network optimization"

Connected through hyperedge HE-3 (induced by source paper "forward and backward information retention for accurate binary neural networks"):
  with members:
    - "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"
    - "dabnn a super fast inference framework for binary neural networks on arm devices"

Connected through hyperedge HE-4 (induced by source paper "binaryduo reducing gradient mismatch in binary activation network by coupling binary activations"):
  with members:
    - "regularizing activation distribution for training binarized deep networks"
    - "understanding straight through estimator in training activation quantized neural nets"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "matrix and tensor decompositions for training binary neural networks"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "structured evolution with compact architectures for scalable policy optimization"
    - "projection convolutional neural networks for 1 bit cnns via discrete back propagation"
    - "training and inference with integers in deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "reactnet towards precise binary neural network with generalized activation functions"):
  with members:
    - "regularizing activation distribution for training binarized deep networks"
    - "accurate and compact convolutional neural networks with trained binarization"
    - "xnor net improved binary neural networks"
    - "dabnn a super fast inference framework for binary neural networks on arm devices"
    - "global sparse momentum sgd for pruning very deep neural networks"
    - "projection convolutional neural networks for 1 bit cnns via discrete back propagation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"

Connected through hyperedge HE-6 (induced by source paper "aggregation signature for small object tracking"):
  with members:
    - "state aware anti drift object tracking"
    - "latent constrained correlation filter"
    - "distractor aware siamese networks for visual object tracking"
    - "the world of fast moving objects"
    - "robust estimation of similarity transformation for visual object tracking"
    - "eco efficient convolution operators for tracking"
    - "discriminative scale space tracking"
    - "learning spatially regularized correlation filters for visual tracking"

Connected through hyperedge HE-7 (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"
    - "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"
    - "relaxed quantization for discretized neural networks"

Connected through hyperedge HE-8 (induced by source paper "class dependent compression of deep neural networks"):
  with members:
    - "knowledge squeezed adversarial network compression"
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "exploring randomly wired neural networks for image recognition"
    - "discovering neural wirings"
    - "class balanced loss based on effective number of samples"
    - "darc differentiable architecture compression"
    - "compressing gans using knowledge distillation"
    - "espnetv2 a light weight power efficient and general purpose convolutional neural network"

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)