human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "ntu rgb d a large scale dataset for 3d human activity analysis"):
  Abstract: Recent approaches in depth-based human activity analysis achieved outstanding performance and proved the effectiveness of 3D representation for classification of action classes. Currently available depth-based and RGB+D-based action recognition benchmarks have a number of limitations, including the lack of training samples, distinct class labels, camera views and variety of subjects. In this paper we introduce a large-scale dataset for RGB+D human action recognition with more than 56 thousand video samples and 4 million frames, collected from 40 distinct subjects. Our dataset contains 60 different action classes including daily, mutual, and health-related actions. In addition, we propose a new recurrent neural network structure to model the long-term temporal correlation of the features for each body part, and utilize them for better action classification. Experimental results show the advantages of applying deep learning methods over state-of-the-art hand-crafted features on the suggested cross-subject and cross-view evaluation criteria for our dataset. The introduction of this large scale dataset will enable the community to apply, develop and adapt various data-hungry learning techniques for the task of depth-based and RGB+D-based human activity analysis.

Includes members:
  Member "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "predictively encoded graph convolutional network for noise robust skeleton based action recognition")
  Member "deep multimodal feature analysis for action recognition in rgb d videos":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "infrared and 3d skeleton feature fusion for rgb d action recognition")
  Member "space time representation of people based on 3d skeletal data a review":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a fine to coarse convolutional neural network for 3d human action recognition")
  Member "visualizing and understanding recurrent networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "visual summary of value level feature attribution in prediction classes with recurrent neural networks")
  Member "multimodal multipart learning for action recognition in depth videos":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "action machine rethinking action recognition in trimmed videos")
  Member "differential recurrent neural networks for action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "finehand learning hand shapes for american sign language recognition")
  Member "3d human activity recognition with reconfigurable convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "convolutional neural networks for sentiment analysis in persian social media")
  Member "histogram of oriented principal components for cross view action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "gimme signals discriminative signal encoding for multimodal activity recognition")

Question: Please tell me which class the source paper 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)