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: "hierarchical binary cnns for landmark localization with limited resources"
  Abstract: Our goal is to design architectures that retain the groundbreaking performance of Convolutional Neural Networks (CNNs) for landmark localization and at the same time are lightweight, compact and suitable for applications with limited computational resources. To this end, we make the following contributions: (a) we are the first to study the effect of neural network binarization on localization tasks, namely human pose estimation and face alignment. We exhaustively evaluate various design choices, identify performance bottlenecks, and more importantly propose multiple orthogonal ways to boost performance. (b) Based on our analysis, we propose a novel hierarchical, parallel and multi-scale residual architecture that yields large performance improvement over the standard bottleneck block while having the same number of parameters, thus bridging the gap between the original network and its binarized counterpart. (c) We perform a large number of ablation studies that shed light on the properties and the performance of the proposed block. (d) We present results for experiments on the most challenging datasets for human pose estimation and face alignment, reporting in many cases state-of-the-art performance. (e) We further provide additional results for the problem of facial part segmentation. Code can be downloaded from  https://www.adrianbulat.com/binary-cnn-landmarks .

Connected through hyperedge HE-1 (induced by source paper "discrimination aware network pruning for deep model compression"):
  with members:
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "gate decorator global filter pruning method for accelerating deep convolutional neural networks"
    - "auto embedding generative adversarial networks for high resolution image synthesis"
    - "importance estimation for neural network pruning"
    - "nat neural architecture transformer for accurate and compact architectures"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "pytorch an imperative style high performance deep learning library"
    - "netadapt platform aware neural network adaptation for mobile applications"

Connected through hyperedge HE-2 (induced by source paper "xnor net improved binary neural networks"):
  with members:
    - "t net parametrizing fully convolutional nets with a single high order tensor"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "yolov3 an incremental improvement"
    - "syq learning symmetric quantization for efficient deep neural networks"
    - "training competitive binary neural networks from scratch"
    - "wrpn training and inference using wide reduced precision networks"
    - "towards accurate binary convolutional neural network"

Connected through hyperedge HE-3 (induced by source paper "improved training of binary networks for human pose estimation and image recognition"):
  with members:
    - "quantized densely connected u nets for efficient landmark localization"
    - "multi scale structure aware network for human pose estimation"
    - "simple baselines for human pose estimation and tracking"
    - "learning feature pyramids for human pose estimation"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"
    - "binarized convolutional landmark localizers for human pose estimation and face alignment with limited resources"
    - "detect and track efficient pose estimation in videos"

Connected through hyperedge HE-4 (induced by source paper "facial age estimation by deep residual decision making"):
  with members:
    - "visualizing the decision making process in deep neural decision forest"
    - "interpreting cnns via decision trees"
    - "interpretable learning for self driving cars by visualizing causal attention"
    - "visualizing the loss landscape of neural nets"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "label distribution learning forests"
    - "interpretable convolutional neural networks"
    - "deep regression forests for age estimation"

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)