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: "faster than real time facial alignment a 3d spatial transformer network approach in unconstrained poses"
  Abstract: Facial alignment involves finding a set of landmark points on an image with a known semantic meaning. However, this semantic meaning of landmark points is often lost in 2D approaches where landmarks are either moved to visible boundaries or ignored as the pose of the face changes. In order to extract consistent alignment points across large poses, the 3D structure of the face must be considered in the alignment step. However, extracting a 3D structure from a single 2D image usually requires alignment in the first place. We present our novel approach to simultaneously extract the 3D shape of the face and the semantically consistent 2D alignment through a 3D Spatial Transformer Network (3DSTN) to model both the camera projection matrix and the warping parameters of a 3D model. By utilizing a generic 3D model and a Thin Plate Spline (TPS) warping function, we are able to generate subject specific 3D shapes without the need for a large 3D shape basis. In addition, our proposed network can be trained in an end-to-end framework on entirely synthetic data from the 300W-LP dataset. Unlike other 3D methods, our approach only requires one pass through the network resulting in a faster than real-time alignment. Evaluations of our model on the Annotated Facial Landmarks in the Wild (AFLW) and AFLW2000-3D datasets show our method achieves state-of-the-art performance over other 3D approaches to alignment.

Connected through hyperedge HE-1 (induced by source paper "sm nas structural to modular neural architecture search for object detection"):
  with members:
    - "thundernet towards real time generic object detection"
    - "region proposal by guided anchoring"
    - "detnas backbone search for object detection"
    - "revisiting feature alignment for one stage object detection"
    - "partial order pruning for best speed accuracy trade off in neural architecture search"
    - "nas fcos fast neural architecture search for object detection"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "feature selective anchor free module for single shot object detection"

Connected through hyperedge HE-2 (induced by source paper "feature selective anchor free module for single shot object detection"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "bounding box regression with uncertainty for accurate object detection"
    - "cirl controllable imitative reinforcement learning for vision based self driving"
    - "seeing small faces from robust anchor s perspective"
    - "m2det a single shot object detector based on multi level feature pyramid network"
    - "detnet a backbone network for object detection"
    - "ring loss convex feature normalization for face recognition"
    - "sface an efficient network for face detection in large scale variations"

Connected through hyperedge HE-3 (induced by source paper "lprnet lightweight deep network by low rank pointwise residual convolution"):
  with members:
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "espnetv2 a light weight power efficient and general purpose convolutional neural network"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "mnasnet platform aware neural architecture search for mobile"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "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-4 (induced by source paper "joint face detection and facial motion retargeting for multiple faces"):
  with members:
    - "face alignment in full pose range a 3d total solution"
    - "mobileface 3d face reconstruction with efficient cnn regression"
    - "yolov3 an incremental improvement"
    - "multiposenet fast multi person pose estimation using pose residual network"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "self supervised cnn for unconstrained 3d facial performance capture from an rgb d camera"
    - "nonlinear 3d face morphable model"
    - "learning dense facial correspondences in unconstrained images"

Connected through hyperedge HE-5 (induced by source paper "3d dense face alignment via graph convolution networks"):
  with members:
    - "generating 3d faces using convolutional mesh autoencoders"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "learning dense facial correspondences in unconstrained images"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "dense face alignment"
    - "geometric deep learning going beyond euclidean data"
    - "instance normalization the missing ingredient for fast stylization"

Connected through hyperedge HE-6 (induced by source paper "real time memory efficient large pose face alignment via deep evolutionary network"):
  with members:
    - "face alignment in full pose range a 3d total solution"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "style aggregated network for facial landmark detection"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "dense face alignment"

Connected through hyperedge HE-7 (induced by source paper "validation loss for landmark detection"):
  with members:
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "style aggregated network for facial landmark detection"
    - "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"
    - "dense face alignment"
    - "godp globally optimized dual pathway deep network architecture for facial landmark localization in the wild"

Connected through hyperedge HE-8 (induced by source paper "linearized multi sampling for differentiable image transformation"):
  with members:
    - "lf net learning local features from images"
    - "path aggregation network for instance segmentation"
    - "hierarchical spatial transformer network"
    - "controllable top down feature transformer"
    - "polar transformer networks"
    - "repeatability is not enough learning affine regions via discriminability"
    - "deformable convolutional networks"
    - "lift learned invariant feature transform"

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)