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: "spontaneous facial micro expression recognition using discriminative spatiotemporal local binary pattern with an improved integral projection"
  Abstract: Recently, there are increasing interests in inferring mirco-expression from facial image sequences. Due to subtle facial movement of micro-expressions, feature extraction has become an important and critical issue for spontaneous facial micro-expression recognition. Recent works usually used spatiotemporal local binary pattern for micro-expression analysis. However, the commonly used spatiotemporal local binary pattern considers dynamic texture information to represent face images while misses the shape attribute of face images. On the other hand, their works extracted the spatiotemporal features from the global face regions, which ignore the discriminative information between two micro-expression classes. The above-mentioned problems seriously limit the application of spatiotemporal local binary pattern on micro-expression recognition. In this paper, we propose a discriminative spatiotemporal local binary pattern based on an improved integral projection to resolve the problems of spatiotemporal local binary pattern for micro-expression recognition. Firstly, we develop an improved integral projection for preserving the shape attribute of micro-expressions. Furthermore, an improved integral projection is incorporated with local binary pattern operators across spatial and temporal domains. Specifically, we extract the novel spatiotemporal features incorporating shape attributes into spatiotemporal texture features. For increasing the discrimination of micro-expressions, we propose a new feature selection based on Laplacian method to extract the discriminative information for facial micro-expression recognition. Intensive experiments are conducted on three availably published micro-expression databases. We compare our method with the state-of-the-art algorithms. Experimental results demonstrate that our proposed method achieves promising performance for micro-expression recognition.

Connected through hyperedge HE-1 (induced by source paper "enriched long term recurrent convolutional network for facial micro expression recognition"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "less is more micro expression recognition from video using apex frame"
    - "towards reading hidden emotions a comparative study of spontaneous micro expression spotting and recognition methods"
    - "objective classes for micro facial expression recognition"
    - "sparsity in dynamics of spontaneous subtle emotions analysis and application"
    - "spontaneous subtle expression detection and recognition based on facial strain"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "advanced local motion patterns for macro and micro facial expression recognition"):
  with members:
    - "a deep learning perspective on the origin of facial expressions"
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"
    - "going deeper in facial expression recognition using deep neural networks"
    - "reading hidden emotions spontaneous micro expression spotting and recognition"
    - "automatic facial expression recognition using features of salient facial patches"
    - "epicflow edge preserving interpolation of correspondences for optical flow"
    - "geometric feature based facial expression recognition in image sequences using multi class adaboost and support vector machines"

Connected through hyperedge HE-3 (induced by source paper "a review on facial micro expressions analysis datasets features and metrics"):
  with members:
    - "less is more micro expression recognition from video using apex frame"
    - "towards reading hidden emotions a comparative study of spontaneous micro expression spotting and recognition methods"
    - "objective classes for micro facial expression recognition"
    - "learning a target sample re generator for cross database micro expression recognition"
    - "going deeper with convolutions"
    - "learning spatiotemporal features with 3d convolutional networks"

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)