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: "recurrent convolutional neural network regression for continuous pain intensity estimation in video"
  Abstract: Automatic pain intensity estimation possesses a significant position in healthcare and medical field. Traditional static methods prefer to extract features from frames separately in a video, which would result in unstable changes and peaks among adjacent frames. To overcome this problem, we propose a real-time regression framework based on the recurrent convolutional neural network for automatic frame-level pain intensity estimation. Given vector sequences of AAM-warped facial images, we used a sliding-window strategy to obtain fixed-length input samples for the recurrent network. We then carefully design the architecture of the recurrent network to out-

Connected through hyperedge HE-1 (induced by source paper "emopain challenge 2020 multimodal pain evaluation from facial and bodily expressions"):
  with members:
    - "avec 2019 workshop and challenge state of mind detecting depression with ai and cross cultural affect recognition"
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (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"
    - "sparsity in dynamics of spontaneous subtle emotions analysis and application"
    - "spontaneous subtle expression detection and recognition based on facial strain"
    - "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-3 (induced by source paper "dynamics are important for the recognition of equine pain in video"):
  with members:
    - "what have we learned from deep representations for action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "temporal segment networks towards good practices for deep action recognition"
    - "videolstm convolves attends and flows for action recognition"
    - "beyond short snippets deep networks for video classification"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "action recognition using visual attention"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-4 (induced by source paper "deep weakly supervised domain adaptation for pain localization in videos"):
  with members:
    - "deep spatiotemporal representation of the face for automatic pain intensity estimation"
    - "multi instance dynamic ordinal random fields for weakly supervised facial behavior analysis"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "multiple instance learning a survey of problem characteristics and applications"
    - "fusing deep learned and hand crafted features of appearance shape and dynamics for automatic pain estimation"
    - "learning pain from action unit combinations a weakly supervised approach via multiple instance learning"
    - "regularizing face verification nets for pain intensity regression"
    - "joint face detection and alignment using multitask cascaded convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "a recurrent convolutional neural network approach for sensorless force estimation in robotic surgery"):
  with members:
    - "deep neural networks for no reference and full reference image quality assessment"
    - "methods for interpreting and understanding deep neural networks"
    - "lstm a search space odyssey"
    - "attention clusters purely attention based local feature integration for video classification"
    - "unsupervised learning for physical interaction through video prediction"
    - "unsupervised monocular depth estimation with left right consistency"
    - "image to image translation with conditional adversarial networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-6 (induced by source paper "saliency supervision an intuitive and effective approach for pain intensity regression"):
  with members:
    - "in defense of the triplet loss for person re identification"
    - "eac net a region based deep enhancing and cropping approach for facial action unit detection"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "semantic image inpainting with perceptual and contextual losses"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "facenet a unified embedding for face recognition and clustering"
    - "fast r cnn"

Connected through hyperedge HE-7 (induced by source paper "deep spatiotemporal representation of the face for automatic pain intensity estimation"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "human action recognition using factorized spatio temporal convolutional networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "two stream convolutional networks for action recognition in videos"
    - "practical recommendations for gradient based training of deep architectures"

Connected through hyperedge HE-8 (induced by source paper "multi instance dynamic ordinal random fields for weakly supervised facial behavior analysis"):
  with members:
    - "incremental boosting convolutional neural network for facial action unit recognition"
    - "multi instance dynamic ordinal random fields for weakly supervised pain intensity estimation"
    - "variable state latent conditional random fields for facial expression recognition and action unit detection"
    - "multiple instance learning by discriminative training of markov networks"
    - "fast exact inference for recursive cardinality models"

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)