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: "binarized convolutional landmark localizers for human pose estimation and face alignment with limited resources"
  Abstract: Our goal is to design architectures that retain the groundbreaking performance of 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. Code can be downloaded from https://www.adrianbulat.com/binary-cnn-landmarks

Connected through hyperedge HE-1 (induced by source paper "bats binary architecture search"):
  with members:
    - "learning architectures for binary networks"
    - "circulant binary convolutional networks enhancing the performance of 1 bit dcnns with circulant back propagation"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "lightweight 3d human pose estimation network training using teacher student learning"):
  with members:
    - "learnable triangulation of human pose"
    - "improved training of binary networks for human pose estimation and image recognition"
    - "self supervised learning of 3d human pose using multi view geometry"
    - "xnect real time multi person 3d human pose estimation with a single rgb camera"
    - "deep high resolution representation learning for human pose estimation"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "ordinal depth supervision for 3d human pose estimation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-3 (induced by source paper "hierarchical binary cnns for landmark localization with limited resources"):
  with members:
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "godp globally optimized dual pathway deep network architecture for facial landmark localization in the wild"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "deep neural networks are robust to weight binarization and other non linear distortions"
    - "aggregated residual transformations for deep neural networks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"

Connected through hyperedge HE-4 (induced by source paper "toward fast and accurate human pose estimation via soft gated skip connections"):
  with members:
    - "anchor loss modulating loss scale based on prediction difficulty"
    - "deep high resolution representation learning for human pose estimation"
    - "quantized densely connected u nets for efficient landmark localization"
    - "cu net coupled u nets"
    - "multi scale structure aware network for human pose estimation"
    - "learning feature pyramids for human pose estimation"
    - "self adversarial training for human pose estimation"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-5 (induced by source paper "multistage model for robust face alignment using deep neural networks"):
  with members:
    - "joint face alignment and 3d face reconstruction with application to face recognition"
    - "face alignment using a 3d deeply initialized ensemble of regression trees"
    - "look at boundary a boundary aware face alignment algorithm"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "style aggregated network for facial landmark detection"
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"

Connected through hyperedge HE-6 (induced by source paper "repose learning deep kinematic priors for fast human pose estimation"):
  with members:
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "fast human pose estimation"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"
    - "multi context attention for human pose estimation"
    - "stacked hourglass networks for human pose estimation"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "structured feature learning for pose estimation"
    - "deepercut a deeper stronger and faster multi person pose estimation model"

Connected through hyperedge HE-7 (induced by source paper "semanticadv generating adversarial examples via attribute conditional image editing"):
  with members:
    - "meshadv adversarial meshes for visual recognition"
    - "semantic adversarial attacks parametric transformations that fool deep classifiers"
    - "big but imperceptible adversarial perturbations via semantic manipulation"
    - "spatially transformed adversarial examples"
    - "cosface large margin cosine loss for deep face recognition"
    - "accelerated training for massive classification via dynamic class selection"
    - "large scale gan training for high fidelity natural image synthesis"
    - "attacks meet interpretability attribute steered detection of adversarial samples"

Connected through hyperedge HE-8 (induced by source paper "face detection with feature pyramids and landmarks"):
  with members:
    - "gluoncv and gluonnlp deep learning in computer vision and natural language processing"
    - "robust and high performance face detector"
    - "self driving cars a survey"
    - "refineface refinement neural network for high performance face detection"
    - "searching for mobilenetv3"
    - "pyramidbox high performance detector for finding tiny face"
    - "extd extremely tiny face detector via iterative filter reuse"
    - "retinaface single stage dense face localisation in the wild"

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)