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: "face alignment using k cluster regression forests with weighted splitting"
  Abstract: In this letter, we present a face alignment pipeline based on two novel methods: weighted splitting for K-cluster Regression Forests (KRF) and three-dimensional Affine Pose Regression (3D-APR) for face shape initialization. Our face alignment method is based on the Local Binary Feature (LBF) framework, where instead of standard regression forests and pixel difference features used in the original method, we use our K-Cluster Regression Forests with Weighted Splitting (KRFWS) and Pyramid Histogram of Oriented Gradients (PHOG) features. We also use KRFWS to perform APR and 3D-APR, which intend to improve the face shape initialization. APR applies a rigid 2-D transform to the initial face shape that compensates for inaccuracy in the initial face location, size, and in-plane rotation. 3D-APR estimates the parameters of a 3-D transform that additionally compensates for out-of-plane rotation. The resulting pipeline, consisting of APR and 3D-APR followed by face alignment, shows an improvement of 20% over standard LBF on the challenging Intelligent Behaviour Understanding Group (IBUG) dataset, and state-of-the-art accuracy on the entire 300-W dataset.

Connected through hyperedge HE-1 (induced by source paper "face alignment using a 3d deeply initialized ensemble of regression trees"):
  with members:
    - "face alignment in full pose range a 3d total solution"
    - "look at boundary a boundary aware face alignment algorithm"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "pose invariant face alignment with a single cnn"
    - "binarized convolutional landmark localizers for human pose estimation and face alignment with limited resources"
    - "robust facial landmark detection under significant head poses and occlusion"
    - "face alignment in the wild a survey"

Connected through hyperedge HE-2 (induced by source paper "facial landmark point localization using coarse to fine deep recurrent neural network"):
  with members:
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "improving landmark localization with semi supervised learning"
    - "adversarial learning of structure aware fully convolutional networks for landmark localization"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "recurrent human pose estimation"
    - "robust face alignment using a mixture of invariant experts"
    - "learning deep representation for face alignment with auxiliary attributes"
    - "two stage convolutional part heatmap regression for the 1st 3d face alignment in the wild 3dfaw challenge"

Connected through hyperedge HE-3 (induced by source paper "holoface augmenting human to human interactions on hololens"):
  with members:
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "pose invariant face alignment with a single cnn"
    - "binarized convolutional landmark localizers for human pose estimation and face alignment with limited resources"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "wider face a face detection benchmark"
    - "growing regression forests by classification applications to object pose estimation"

Connected through hyperedge HE-4 (induced by source paper "facial landmarks detection by self iterative regression based landmarks attention network"):
  with members:
    - "human pose estimation with iterative error feedback"
    - "an empirical study of recent face alignment methods"
    - "very deep convolutional networks for large scale image recognition"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-5 (induced by source paper "deep alignment network a convolutional neural network for robust face alignment"):
  with members:
    - "robust face alignment using a mixture of invariant experts"
    - "two stage convolutional part heatmap regression for the 1st 3d face alignment in the wild 3dfaw challenge"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

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)