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 feature tracking under varying facial expressions and face poses based on restricted boltzmann machines"
  Abstract: Facial feature tracking is an active area in computer vision due to its relevance to many applications. It is a nontrivial task, since faces may have varying facial expressions, poses or occlusions. In this paper, we address this problem by proposing a face shape prior model that is constructed based on the Restricted Boltzmann Machines (RBM) and their variants. Specifically, we first construct a model based on Deep Belief Networks to capture the face shape variations due to varying facial expressions for near-frontal view. To handle pose variations, the frontal face shape prior model is incorporated into a 3-way RBM model that could capture the relationship between frontal face shapes and non-frontal face shapes. Finally, we introduce methods to systematically combine the face shape prior models with image measurements of facial feature points. Experiments on benchmark databases show that with the proposed method, facial feature points can be tracked robustly and accurately even if faces have significant facial expressions and poses.

Connected through hyperedge HE-1 (induced by source paper "facial landmark detection a literature survey"):
  with members:
    - "a comprehensive performance evaluation of deformable face tracking in the wild"
    - "constrained joint cascade regression framework for simultaneous facial action unit recognition and facial landmark detection"
    - "robust facial landmark detection under significant head poses and occlusion"
    - "a hierarchical probabilistic model for facial feature detection"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "learning deep representation for face alignment with auxiliary attributes"
    - "deepid3 face recognition with very deep neural networks"
    - "facenet a unified embedding for face recognition and clustering"

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"
    - "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"
    - "bidirectional warping of active appearance model"
    - "deep lambertian networks"

Connected through hyperedge HE-3 (induced by source paper "deep recurrent regression for facial landmark detection"):
  with members:
    - "learning deep representation for face alignment with auxiliary attributes"
    - "facial landmark detection using structured output deep neural networks"
    - "learning deconvolution network for semantic segmentation"
    - "facial feature point detection a comprehensive survey"
    - "very deep convolutional networks for large scale image recognition"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "show and tell a neural image caption generator"

Connected through hyperedge HE-4 (induced by source paper "deep cascaded regression for face alignment"):
  with members:
    - "very deep convolutional networks for large scale image recognition"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"

Connected through hyperedge HE-5 (induced by source paper "a generative restricted boltzmann machine based method for high dimensional motion data modeling"):
  with members:
    - "emotional expression classification using time series kernels"
    - "classification of sets using restricted boltzmann machines"

Connected through hyperedge HE-6 (induced by source paper "an empirical study of recent face alignment methods"):
  with members:
    - "pose invariant 3d face alignment"
    - "facial feature point detection a comprehensive survey"

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)