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: "deep regression for face alignment"
  Abstract: In this paper, we present a deep regression approach for face alignment. The deep architecture consists of a global layer and multi-stage local layers. We apply the back-propagation algorithm with the dropout strategy to jointly optimize the regression parameters. We show that the resulting deep regressor gradually and evenly approaches the true facial landmarks stage by stage, avoiding the tendency to yield over-strong early stage regressors while over-weak later stage regressors. Experimental results show that our approach achieves the state-of-the-art

Connected through hyperedge HE-1 (induced by source paper "a fast online cascaded regression algorithm for face alignment"):
  with members:
    - "3 d personvlad learning deep global representations for video based person reidentification"
    - "a robust approach for securing audio classification against adversarial attacks"
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "cross entropy adversarial view adaptation for person re identification"
    - "face alignment in full pose range a 3d total solution"
    - "robust tracking via weighted online extreme learning machine"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "a functional regression approach to facial landmark tracking"

Connected through hyperedge HE-2 (induced by source paper "quantized densely connected u nets for efficient landmark localization"):
  with members:
    - "learning to forecast and refine residual motion for image to video generation"
    - "red net a recurrent encoder decoder network for video based face alignment"
    - "training and inference with integers in deep neural networks"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "deeprebirth accelerating deep neural network execution on mobile devices"
    - "learning feature pyramids for human pose estimation"
    - "memory efficient implementation of densenets"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"

Connected through hyperedge HE-3 (induced by source paper "learning deep representation for face alignment with auxiliary attributes"):
  with members:
    - "surpassing human level face verification performance on lfw with gaussianface"
    - "recover canonical view faces in the wild with deep neural networks"
    - "deep learning face representation by joint identification verification"
    - "deep learning multi view representation for face recognition"
    - "heterogeneous multi task learning for human pose estimation with deep convolutional neural network"
    - "a convex formulation for learning task relationships in multi task learning"

Connected through hyperedge HE-4 (induced by source paper "globally tuned cascade pose regression via back propagation with application in 2d face pose estimation and heart segmentation in 3d ct images"):
  with members:
    - "deeppose human pose estimation via deep neural networks"
    - "deep inside convolutional networks visualising image classification models and saliency maps"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

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)