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: "multimodal multipart learning for action recognition in depth videos"
  Abstract: The articulated and complex nature of human actions makes the task of action recognition difficult. One approach to handle this complexity is dividing it to the kinetics of body parts and analyzing the actions based on these partial descriptors. We propose a joint sparse regression based learning method which utilizes the structured sparsity to model each action as a combination of multimodal features from a sparse set of body parts. To represent dynamics and appearance of parts, we employ a heterogeneous set of depth and skeleton based features. The proper structure of multimodal multipart features are formulated into the learning framework via the proposed hierarchical mixed norm, to regularize the structured features of each part and to apply sparsity between them, in favor of a group feature selection. Our experimental results expose the effectiveness of the proposed learning method in which it outperforms other methods in all three tested datasets while saturating one of them by achieving perfect accuracy.

Connected through hyperedge HE-1 (induced by source paper "action machine rethinking action recognition in trimmed videos"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "a short note about kinetics 600"
    - "simple baselines for human pose estimation and tracking"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "glimpse clouds human activity recognition from unstructured feature points"
    - "view adaptive neural networks for high performance skeleton based human action recognition"
    - "the activitynet large scale activity recognition challenge 2018 summary"
    - "bsn boundary sensitive network for temporal action proposal generation"

Connected through hyperedge HE-2 (induced by source paper "first person hand action benchmark with rgb d videos and 3d hand pose annotations"):
  with members:
    - "augmented skeleton space transfer for depth based hand pose estimation"
    - "learning and refining of privileged information based rnns for action recognition from depth sequences"
    - "bighand2 2m benchmark hand pose dataset and state of the art analysis"
    - "the 2017 hands in the million challenge on 3d hand pose estimation"
    - "real time hand tracking under occlusion from an egocentric rgb d sensor"
    - "depth based 3d hand pose estimation from current achievements to future goals"
    - "sembed semantic embedding of egocentric action videos"
    - "convolutional pose machines"

Connected through hyperedge HE-3 (induced by source paper "spatio temporal lstm with trust gates for 3d human action recognition"):
  with members:
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "gated siamese convolutional neural network architecture for human re identification"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"
    - "online human action detection using joint classification regression recurrent neural networks"
    - "deep multimodal feature analysis for action recognition in rgb d videos"
    - "a siamese long short term memory architecture for human re identification"
    - "a deep structured model with radius margin bound for 3d human activity recognition"
    - "space time representation of people based on 3d skeletal data a review"

Connected through hyperedge HE-4 (induced by source paper "ntu rgb d a large scale dataset for 3d human activity analysis"):
  with members:
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"
    - "deep multimodal feature analysis for action recognition in rgb d videos"
    - "space time representation of people based on 3d skeletal data a review"
    - "visualizing and understanding recurrent networks"
    - "differential recurrent neural networks for action recognition"
    - "3d human activity recognition with reconfigurable convolutional neural networks"
    - "histogram of oriented principal components for cross view action recognition"
    - "long term recurrent convolutional networks for visual recognition and description"

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)