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: "rgb d based action recognition datasets a survey"
  Abstract: Human action recognition from RGB-D (Red, Green, Blue and Depth) data has attracted increasing attention since the first work reported in 2010. Over this period, many benchmark datasets have been created to facilitate the development and evaluation of new algorithms. This raises the question of which dataset to select and how to use it in providing a fair and objective comparative evaluation against state-of-the-art methods. To address this issue, this paper provides a comprehensive review of the most commonly used action recognition related RGB-D video datasets, including 27 single-view datasets, 10 multi-view datasets, and 7 multi-person datasets. The detailed information and analysis of these datasets is a useful resource in guiding insightful selection of datasets for future research. In addition, the issues with current algorithm evaluation vis-\'{a}-vis limitations of the available datasets and evaluation protocols are also highlighted; resulting in a number of recommendations for collection of new datasets and use of evaluation protocols.

Connected through hyperedge HE-1 (induced by source paper "human activity recognition from skeleton poses"):
  with members:
    - "a systematic comparison of supervised classifiers"
    - "unstructured human activity detection from rgbd images"

Connected through hyperedge HE-2 (induced by source paper "scene flow to action map a new representation for rgb d based action recognition with convolutional neural networks"):
  with members:
    - "large scale isolated gesture recognition using convolutional neural networks"
    - "rank pooling for action recognition"
    - "joint distance maps based action recognition with convolutional neural networks"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "large scale continuous gesture recognition using convolutional neural networks"
    - "spatio temporal lstm with trust gates for 3d human action recognition"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"
    - "action recognition based on joint trajectory maps using convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "pku mmd a large scale benchmark for continuous multi modal human action understanding"):
  with members:
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "online action detection"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"
    - "an end to end spatio temporal attention model for human action recognition from skeleton data"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "temporal segment networks towards good practices for deep action recognition"
    - "modeling spatial temporal clues in a hybrid deep learning framework for video classification"
    - "3d human activity recognition with reconfigurable convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "cooperative training of deep aggregation networks for rgb d action recognition"):
  with members:
    - "scene flow to action map a new representation for rgb d based action recognition with convolutional neural networks"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "deep multimodal feature analysis for action recognition in rgb d videos"
    - "facenet a unified embedding for face recognition and clustering"
    - "a generative restricted boltzmann machine based method for high dimensional motion data modeling"
    - "slow and steady feature analysis higher order temporal coherence in video"
    - "very deep convolutional networks for large scale image recognition"
    - "long term recurrent convolutional networks for visual recognition and description"

Connected through hyperedge HE-5 (induced by source paper "rgbd datasets past present and future"):
  with members:
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "a large dataset of object scans"
    - "a dataset of naturally occurring whole body background activity to reduce gesture conflicts"
    - "a dataset for improved rgbd based object detection and pose estimation for warehouse pick and place"
    - "scenenet understanding real world indoor scenes with synthetic data"
    - "3d human activity recognition with reconfigurable convolutional neural networks"
    - "cross view action modeling learning and recognition"
    - "the role of rgb d benchmark datasets an overview"

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)