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: "facial landmark detection with tweaked convolutional neural networks"
  Abstract: We present a novel convolutional neural network (CNN) design for facial landmark coordinate regression. We examine the intermediate features of a standard CNN trained for landmark detection and show that features extracted from later, more specialized layers capture rough landmark locations. This provides a natural means of applying differential treatment midway through the network, tweaking processing based on facial alignment. The resulting Tweaked CNN model (TCNN) harnesses the robustness of CNNs for landmark detection, in an appearance-sensitive manner without training multi-part or multi-scale models. Our results on standard face landmark detection and face verification benchmarks show TCNN to surpasses previously published performances by wide margins.

Connected through hyperedge HE-1 (induced by source paper "mobilefan transferring deep hidden representation for face alignment"):
  with members:
    - "structured knowledge distillation for dense prediction"
    - "look at boundary a boundary aware face alignment algorithm"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "multi label learning based deep transfer neural network for facial attribute classification"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "simple baselines for human pose estimation and tracking"
    - "improving fast segmentation with teacher student learning"

Connected through hyperedge HE-2 (induced by source paper "teacher supervises students how to learn from partially labeled images for facial landmark detection"):
  with members:
    - "network pruning via transformable architecture search"
    - "few example object detection with model communication"
    - "prototype propagation networks ppn for weakly supervised few shot learning on category graph"
    - "quantized densely connected u nets for efficient landmark localization"
    - "autoloss learning discrete schedules for alternate optimization"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "exploring disentangled feature representation beyond face identification"
    - "style aggregated network for facial landmark detection"

Connected through hyperedge HE-3 (induced by source paper "adaloss adaptive loss function for landmark localization"):
  with members:
    - "style aggregated network for facial landmark detection"
    - "supervision by registration an unsupervised approach to improve the precision of facial landmark detectors"
    - "pose invariant face alignment with a single cnn"
    - "improving landmark localization with semi supervised learning"
    - "unite the people closing the loop between 3d and 2d human representations"
    - "stacked hourglass networks for human pose estimation"
    - "an empirical study into annotator agreement ground truth estimation and algorithm evaluation"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "face alignment in full pose range a 3d total solution"):
  with members:
    - "pose invariant face alignment with a single cnn"
    - "finding tiny faces"
    - "unconstrained facial landmark localization with backbone branches fully convolutional networks"
    - "human pose estimation with iterative error feedback"
    - "pose invariant 3d face alignment"
    - "effective face frontalization in unconstrained images"
    - "going deeper with convolutions"
    - "face expression recognition and analysis the state of the art"

Connected through hyperedge HE-5 (induced by source paper "facial image deformation based on landmark detection"):
  with members:
    - "look at boundary a boundary aware face alignment algorithm"
    - "beyond trade off accelerate fcn based face detector with higher accuracy"
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "joint face detection and alignment using multitask cascaded convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "wing loss for robust facial landmark localisation with convolutional neural networks"):
  with members:
    - "evaluation of dense 3d reconstruction from 2d face images in the wild"
    - "faster than real time facial alignment a 3d spatial transformer network approach in unconstrained poses"
    - "sphereface deep hypersphere embedding for face recognition"
    - "end to end 3d face reconstruction with deep neural networks"
    - "pose invariant face alignment with a single cnn"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "constrained joint cascade regression framework for simultaneous facial action unit recognition and facial landmark detection"
    - "dense face alignment"

Connected through hyperedge HE-7 (induced by source paper "visual data augmentation through learning"):
  with members:
    - "learning transferable architectures for scalable image recognition"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "energy based generative adversarial network"
    - "visual dynamics probabilistic future frame synthesis via cross convolutional networks"
    - "image to image translation with conditional adversarial networks"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "context encoders feature learning by inpainting"

Connected through hyperedge HE-8 (induced by source paper "model distillation with knowledge transfer from face classification to alignment and verification"):
  with members:
    - "an analysis of deep neural network models for practical applications"
    - "how to train triplet networks with 100k identities"
    - "deep model compression distilling knowledge from noisy teachers"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "you only look once unified real time object detection"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "facenet a unified embedding for face recognition and clustering"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)