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: "fusing deep learned and hand crafted features of appearance shape and dynamics for automatic pain estimation"
  Abstract: Automatic continuous time, continuous value assessment of a patient's pain from face video is highly sought after by the medical profession. Despite the recent advances in deep learning that attain impressive results in many domains, pain estimation risks not being able to benefit from this due to the difficulty in obtaining data sets of considerable size. In this work we propose a combination of hand-crafted and deep-learned features that makes the most of deep learning techniques in small sample settings. Encoding shape, appearance, and dynamics, our method significantly outperforms the current state of the art, attaining a RMSE error of less than 1 point on a 16-level pain scale, whilst simultaneously scoring a 67.3% Pearson correlation coefficient between our predicted pain level time series and the ground truth.

Connected through hyperedge HE-1 (induced by source paper "deepsat v2 feature augmented convolutional neural nets for satellite image classification"):
  with members:
    - "spacenet a remote sensing dataset and challenge series"
    - "deepsat a learning framework for satellite imagery"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-2 (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"
    - "learning pain from action unit combinations a weakly supervised approach via multiple instance learning"
    - "regularizing face verification nets for pain intensity regression"
    - "recurrent convolutional neural network regression for continuous pain intensity estimation in video"
    - "joint face detection and alignment using multitask cascaded convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "automated pain detection from facial expressions using facs a review"):
  with members:
    - "deep structured learning for facial action unit intensity estimation"
    - "deepfacelift interpretable personalized models for automatic estimation of self reported pain"
    - "learning pain from action unit combinations a weakly supervised approach via multiple instance learning"
    - "lomo latent ordinal model for facial analysis in videos"
    - "machine based multimodal pain assessment tool for infants a review"
    - "very deep convolutional networks for large scale image recognition"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

Connected through hyperedge HE-4 (induced by source paper "deepfacelift interpretable personalized models for automatic estimation of self reported pain"):
  with members:
    - "gradients of counterfactuals"
    - "an overview of multi task learning in deep neural networks"
    - "not just a black box learning important features through propagating activation differences"
    - "visualizing and understanding 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)