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: "bidirectional warping of active appearance model"
  Abstract: Active Appearance Model (AAM) is a commonly used method for facial image analysis with applications in face identification and facial expression recognition. This paper proposes a new approach based on image alignment for AAM fitting called bidirectional warping. Previous approaches warp either the input image or the appearance template. We propose to warp both the input image, using incremental update by an affine transformation, and the appearance template, using an inverse compositional approach. Our experimental results on Multi-PIE face database show that the bidirectional approach outperforms state-of-the-art inverse compositional fitting approaches in extracting landmark points of faces with shape and pose variations.

Connected through hyperedge HE-1 (induced by source paper "going deeper in facial expression recognition using deep neural networks"):
  with members:
    - "deepid3 face recognition with very deep neural networks"
    - "going deeper with convolutions"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"
    - "deeppose human pose estimation via deep neural networks"
    - "expressionbot an emotive lifelike robotic face for face to face communication"
    - "ebear an expressive bear like robot"
    - "provable bounds for learning some deep representations"

Connected through hyperedge HE-2 (induced by source paper "deep appearance models a deep boltzmann machine approach for face modeling"):
  with members:
    - "cms rcnn contextual multi scale region based cnn for unconstrained face detection"
    - "facial feature tracking under varying facial expressions and face poses based on restricted boltzmann machines"
    - "a unified framework for compositional fitting of active appearance models"
    - "learning deep representation for face alignment with auxiliary attributes"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "robust face recognition via multimodal deep face representation"
    - "effective face frontalization in unconstrained images"
    - "deep lambertian networks"

Connected through hyperedge HE-3 (induced by source paper "facial expression recognition from world wild web"):
  with members:
    - "going deeper in facial expression recognition using deep neural networks"
    - "going deeper with convolutions"
    - "training convolutional networks with noisy labels"
    - "deeppose human pose estimation via deep neural networks"
    - "expressionbot an emotive lifelike robotic face for face to face communication"
    - "ebear an expressive bear like robot"
    - "deep learning using linear support vector machines"

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)