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: "less is more micro expression recognition from video using apex frame"
  Abstract: Abstract   Despite recent interest and advances in facial micro-expression research, there is still plenty of room for improvement in terms of micro-expression recognition. Conventional feature extraction approaches for micro-expression video consider either the whole video sequence or a part of it, for representation. However, with the high-speed video capture of micro-expressions (100–200 fps), are all frames necessary to provide a sufficiently meaningful representation? Is the luxury of data a bane to accurate recognition? A novel proposition is presented in this paper, whereby we utilize only two images per video, namely, the apex frame and the onset frame. The apex frame of a video contains the highest intensity of expression changes among all frames, while the onset is the perfect choice of a reference frame with neutral expression. A new feature extractor, Bi-Weighted Oriented Optical Flow (Bi-WOOF) is proposed to encode essential expressiveness of the apex frame. We evaluated the proposed method on five micro-expression databases—CAS(ME)       2     , CASME II, SMIC-HS, SMIC-NIR and SMIC-VIS. Our experiments lend credence to our hypothesis, with our proposed technique achieving a state-of-the-art F1-score recognition performance of 0.61 and 0.62 in the high frame rate CASME II and SMIC-HS databases respectively.

Connected through hyperedge HE-1 (induced by source paper "evaluation of the spatio temporal features and gan for micro expression recognition system"):
  with members:
    - "shallow triple stream three dimensional cnn ststnet for micro expression recognition"
    - "towards reading hidden emotions a comparative study of spontaneous micro expression spotting and recognition methods"
    - "liteflownet a lightweight convolutional neural network for optical flow estimation"
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "generative face completion"
    - "camera style adaptation for person re identification"
    - "sparsity in dynamics of spontaneous subtle emotions analysis and application"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"

Connected through hyperedge HE-2 (induced by source paper "micro expression action unit detection withspatio temporal adaptive pooling"):
  with members:
    - "adaptive pooling operators for weakly labeled sound event detection"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "focal loss for dense object detection"
    - "deep extreme multi label learning"
    - "optimizing filter size in convolutional neural networks for facial action unit recognition"
    - "adascan adaptive scan pooling in deep convolutional neural networks for human action recognition in videos"
    - "contextual action recognition with r cnn"
    - "beyond short snippets deep networks for video classification"

Connected through hyperedge HE-3 (induced by source paper "automatic surface area and volume prediction on ellipsoidal ham using deep learning"):
  with members:
    - "detect and track efficient pose estimation in videos"
    - "mask r cnn"
    - "mask cnn localizing parts and selecting descriptors for fine grained image recognition"
    - "feature pyramid networks for object detection"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-4 (induced by source paper "shallow triple stream three dimensional cnn ststnet for micro expression recognition"):
  with members:
    - "off apexnet on micro expression recognition system"
    - "towards reading hidden emotions a comparative study of spontaneous micro expression spotting and recognition methods"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "a novel apex time network for cross dataset micro expression recognition"):
  with members:
    - "micro attention for micro expression recognition"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-6 (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"
    - "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"
    - "spontaneous facial micro expression recognition using discriminative spatiotemporal local binary pattern with an improved integral projection"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "a survey of automatic facial micro expression analysis databases methods and challenges"):
  with members:
    - "enriched long term recurrent convolutional network for facial micro expression recognition"
    - "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"
    - "learning a target sample re generator for cross database micro expression recognition"
    - "spontaneous subtle expression detection and recognition based on facial strain"
    - "objective micro facial movement detection using facs based regions and baseline evaluation"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-8 (induced by source paper "micro attention for micro expression recognition"):
  with members:
    - "enriched long term recurrent convolutional network for facial micro expression recognition"
    - "towards reading hidden emotions a comparative study of spontaneous micro expression spotting and recognition methods"
    - "objective classes for micro facial expression recognition"
    - "residual attention network for image classification"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image 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)