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 landmarks detection by self iterative regression based landmarks attention network"
  Abstract: Cascaded Regression (CR) based methods have been proposed to solve facial landmarks detection problem, which learn a series of descent directions by multiple cascaded regressors separately trained in coarse and fine stages. They outperform the traditional gradient descent based methods in both accuracy and running speed. However, cascaded regression is not robust enough because each regressor's training data comes from the output of previous regressor. Moreover, training multiple regressors requires lots of computing resources, especially for deep learning based methods. In this paper, we develop a Self-Iterative Regression (SIR) framework to improve the model efficiency. Only one self-iterative regressor is trained to learn the descent directions for samples from coarse stages to fine stages, and parameters are iteratively updated by the same regressor. Specifically, we proposed Landmarks-Attention Network (LAN) as our regressor, which concurrently learns features around each landmark and obtains the holistic location increment. By doing so, not only the rest of regressors are removed to simplify the training process, but the number of model parameters is significantly decreased. The experiments demonstrate that with only 3.72M model parameters, our proposed method achieves the state-of-the-art performance.

Connected through hyperedge HE-1 (induced by source paper "see better before looking closer weakly supervised data augmentation network for fine grained visual classification"):
  with members:
    - "multi attention multi class constraint for fine grained image recognition"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "learning to navigate for fine grained classification"
    - "adversarial complementary learning for weakly supervised object localization"
    - "autoaugment learning augmentation policies from data"
    - "improved bilinear pooling with cnns"
    - "pairwise confusion for fine grained visual classification"
    - "improved regularization of convolutional neural networks with cutout"

Connected through hyperedge HE-2 (induced by source paper "weakly supervised local attention network for fine grained visual classification"):
  with members:
    - "adversarial complementary learning for weakly supervised object localization"
    - "towards faster training of global covariance pooling networks by iterative matrix square root normalization"
    - "mask cnn localizing parts and selecting descriptors for fine grained image recognition"
    - "top down neural attention by excitation backprop"
    - "spatial transformer networks"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-3 (induced by source paper "towards highly accurate and stable face alignment for high resolution videos"):
  with members:
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "fsrnet end to end learning face super resolution with facial priors"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "multi context attention for human pose estimation"
    - "adversarial learning of structure aware fully convolutional networks for landmark localization"
    - "stacked hourglass networks for human pose estimation"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "learning deep representation for face alignment with auxiliary attributes"

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)