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: "sparsity in dynamics of spontaneous subtle emotions analysis and application"
  Abstract: Subtle emotions are present in diverse real-life situations: in hostile environments, enemies and/or spies maliciouslyconceal their emotions as part of their deception; in life-threatening situations, victims under duress have no choice but to withhold theirreal feelings; in the medical scene, patients with psychological conditions such as depression could either be intentionally orsubconsciously suppressing their anguish from loved ones. Under such circumstances, it is often crucial that these subtle emotions arerecognized before it is too late. These spontaneous subtle emotions are typically expressed through micro-expressions, which are tiny,sudden and short-lived dynamics of facial muscles; thus, such micro-expressions pose a great challenge for visual recognition. Theabrupt but significant dynamics for the recognition task are temporally sparse while the rest, i.e. irrelevant dynamics, are temporallyredundant. In this work, we analyze and enforce sparsity constraints to learn significant temporal and spectral structures whileeliminating irrelevant facial dynamics of micro-expressions, which would ease the challenge in the visual recognition of spontaneoussubtle emotions. The hypothesis is confirmed through experimental results of automatic spontaneous subtle emotion recognition withseveral sparsity levels on CASME II and SMIC, the two well-established and publicly available spontaneous subtle emotion databases.The overall performances of the automatic subtle emotion recognition are boosted when only significant dynamics of the originalsequences are preserved.

Connected through hyperedge HE-1 (induced by source paper "spatiotemporal recurrent convolutional networks for recognizing spontaneous micro expressions"):
  with members:
    - "eco efficient convolutional network for online video understanding"
    - "towards reading hidden emotions a comparative study of spontaneous micro expression spotting and recognition methods"
    - "focal loss for dense object detection"
    - "spontaneous subtle expression detection and recognition based on facial strain"
    - "recurrent convolutional neural network regression for continuous pain intensity estimation in video"
    - "temporal segment networks towards good practices for deep action recognition"
    - "unsupervised learning of video representations using lstms"
    - "beyond short snippets deep networks for video classification"

Connected through hyperedge HE-2 (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"
    - "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"
    - "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"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"

Connected through hyperedge HE-3 (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"
    - "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-4 (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"
    - "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"
    - "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-5 (induced by source paper "cross database micro expression recognition a benchmark"):
  with members:
    - "spatiotemporal recurrent convolutional networks for recognizing spontaneous micro expressions"
    - "a boost in revealing subtle facial expressions a consolidated eulerian framework"
    - "towards reading hidden emotions a comparative study of spontaneous micro expression spotting and recognition methods"
    - "learning a target sample re generator for cross database micro expression recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "ucf101 a dataset of 101 human actions classes from videos in the wild"

Connected through hyperedge HE-6 (induced by source paper "less is more micro expression recognition from video using apex frame"):
  with members:
    - "towards reading hidden emotions a comparative study of spontaneous micro expression spotting and recognition methods"
    - "spontaneous subtle expression detection and recognition based on facial strain"

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)