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: "investigation of different skeleton features for cnn based 3d action recognition"
  Abstract: Deep learning techniques are being used in skeleton based action recognition tasks and outstanding performance has been reported. Compared with RNN based methods which tend to overemphasize temporal information, CNN-based approaches can jointly capture spatio-temporal information from texture color images encoded from skeleton sequences. There are several skeleton-based features that have proven effective in RNN-based and handcrafted-feature-based methods. However, it remains unknown whether they are suitable for CNN-based approaches. This paper proposes to encode five spatial skeleton features into images with different encoding methods. In addition, the performance implication of different joints used for feature extraction is studied. The proposed method achieved state-of-the-art performance on NTU RGB+D dataset for 3D human action analysis. An accuracy of 75.32\% was achieved in Large Scale 3D Human Activity Analysis Challenge in Depth Videos.

Connected through hyperedge HE-1 (induced by source paper "a survey on 3d skeleton based action recognition using learning method"):
  with members:
    - "semantics guided neural networks for efficient skeleton based human action recognition"
    - "richlt activated graph convolutional network for action recognition with incomplete skeletons"
    - "make skeleton based action recognition model smaller faster and better"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "skeleton image representation for 3d action recognition based on tree structure and reference joints"
    - "a comparative review of recent kinect based action recognition algorithms"
    - "skelemotion a new representation of skeleton joint sequences based on motion information for 3d action recognition"

Connected through hyperedge HE-2 (induced by source paper "predictively encoded graph convolutional network for noise robust skeleton based action recognition"):
  with members:
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "driver drowsiness detection using condition adaptive representation learning framework"
    - "richlt activated graph convolutional network for action recognition with incomplete skeletons"
    - "learning graph convolutional network for skeleton based human action recognition by neural searching"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "skeleton based action recognition with multi stream adaptive graph convolutional networks"
    - "hallucinating idt descriptors and i3d optical flow features for action recognition with cnns"
    - "densefusion 6d object pose estimation by iterative dense fusion"

Connected through hyperedge HE-3 (induced by source paper "a deep learning approach for real time 3d human action recognition from skeletal data"):
  with members:
    - "learning to recognise 3d human action from a new skeleton based representation using deep convolutional neural networks"
    - "skeletal movement to color map a novel representation for 3d action recognition with inception residual networks"
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"
    - "exploiting deep residual networks for human action recognition from skeletal data"
    - "skeleton based human action recognition with global context aware attention lstm networks"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "spatio temporal lstm with trust gates for 3d human action recognition"

Connected through hyperedge HE-4 (induced by source paper "richlt activated graph convolutional network for action recognition with incomplete skeletons"):
  with members:
    - "part based graph convolutional network for action recognition"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "diversity regularized spatiotemporal attention for video based person re identification"
    - "non local neural networks"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "view adaptive recurrent neural networks for high performance human action recognition from skeleton data"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"

Connected through hyperedge HE-5 (induced by source paper "relational network for skeleton based action recognition"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "recurrent relational networks for complex relational reasoning"
    - "spatio temporal graph convolution for skeleton based action recognition"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"
    - "recurrent relational networks"
    - "a simple neural network module for relational reasoning"
    - "two stream 3d convolutional neural network for skeleton based action recognition"
    - "a new representation of skeleton sequences for 3d action recognition"

Connected through hyperedge HE-6 (induced by source paper "graph distillation for action detection with privileged modalities"):
  with members:
    - "towards vision based smart hospitals a system for tracking and monitoring hand hygiene compliance"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "learning and refining of privileged information based rnns for action recognition from depth sequences"
    - "learning cross modal deep representations for robust pedestrian detection"
    - "temporal action detection with structured segment networks"
    - "miml fcn multi instance multi label learning via fully convolutional networks with privileged information"
    - "unsupervised learning of long term motion dynamics for videos"
    - "joint distance maps based action recognition with convolutional neural 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)