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: "reading hidden emotions spontaneous micro expression spotting and recognition"
  Abstract: Micro-expressions (MEs) are rapid, involuntary facial expressions which reveal emotions that people do not intend to show. Studying MEs is valuable as recognizing them has many important applications, particularly in forensic science and psychotherapy. However, analyzing spontaneous MEs is very challenging due to their short duration and low intensity. Automatic ME analysis includes two tasks: ME spotting and ME recognition.For ME spotting, previous studies have focused on posed rather than spontaneous videos. For ME recognition, the performance of previous studies is low. To address these challenges, we make the following contributions: (i) We propose the first method for spotting spontaneous MEs in long videos (by exploiting feature difference contrast). This method is training free and works on arbitrary unseen videos. (ii) We present an advanced ME recognition framework, which outperforms previous work by a large margin on two challenging spontaneous ME databases (SMIC and CASMEII). (iii) We propose the first automatic ME analysis system (MESR), which can spot and recognize MEs from spontaneous video data. Finally, we show that our method achieves comparable performance to humans at this very challenging task, and outperforms humans in the ME recognition task by a large margin.

Connected through hyperedge HE-1 (induced by source paper "a boost in revealing subtle facial expressions a consolidated eulerian framework"):
  with members:
    - "spatiotemporal recurrent convolutional networks for recognizing spontaneous micro expressions"
    - "cross database micro expression recognition a benchmark"
    - "towards reading hidden emotions a comparative study of spontaneous micro expression spotting and recognition methods"
    - "video acceleration magnification"
    - "micro expression spotting a benchmark"

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"
    - "spontaneous facial micro expression recognition using discriminative spatiotemporal local binary pattern with an improved integral projection"
    - "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 "objective classes for micro facial expression recognition"):
  with members:
    - "less is more micro expression recognition from video using apex frame"
    - "objective micro facial movement detection using facs based regions and baseline evaluation"
    - "ucf101 a dataset of 101 human actions classes from videos in the wild"

Connected through hyperedge HE-4 (induced by source paper "distinguishing posed and spontaneous smiles by facial dynamics"):
  with members:
    - "spontaneous vs posed smiles can we tell the difference"
    - "when face recognition meets with deep learning an evaluation of convolutional neural networks for face 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)