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: "skeleton based action recognition using lstm and cnn"
  Abstract: Recent methods based on 3D skeleton data have achieved outstanding performance due to its conciseness, robustness, and view-independent representation. With the development of deep learning, Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM)-based learning methods have achieved promising performance for action recognition. However, for CNN-based methods, it is inevitable to loss temporal information when a sequence is encoded into images. In order to capture as much spatial-temporal information as possible, LSTM and CNN are adopted to conduct effective recognition with later score fusion. In addition, experimental results show that the score fusion between CNN and LSTM performs better than that between LSTM and LSTM for the same feature. Our method achieved state-of-the-art results on NTU RGB+D datasets for 3D human action analysis. The proposed method achieved 87.40% in terms of accuracy and ranked $1^{st}$ place in Large Scale 3D Human Activity Analysis Challenge in Depth Videos.

Connected through hyperedge HE-1 (induced by source paper "deep aligned convolutional neural network for skeleton based action recognition and segmentation"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"
    - "skeleton based action recognition using spatio temporal lstm network with trust gates"
    - "convolutional sequence to sequence learning"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "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-2 (induced by source paper "learning coupled spatial temporal attention for skeleton based action recognition"):
  with members:
    - "action recognition with spatio temporal visual attention on skeleton image sequences"
    - "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"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"
    - "skeleton based action recognition using spatio temporal lstm network with trust gates"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "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-3 (induced by source paper "make skeleton based action recognition model smaller faster and better"):
  with members:
    - "can wifi estimate person pose"
    - "simple yet efficient real time pose based action recognition"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "simple baselines for human pose estimation and tracking"
    - "slowfast networks for video recognition"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"
    - "vnect real time 3d human pose estimation with a single rgb camera"
    - "chained multi stream networks exploiting pose motion and appearance for action classification and detection"

Connected through hyperedge HE-4 (induced by source paper "a large scale varying view rgb d action dataset for arbitrary view human action recognition"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "temporal convolutional networks for action segmentation and detection"
    - "histogram of oriented principal components for cross view action recognition"
    - "long term recurrent convolutional networks for visual recognition and description"

Connected through hyperedge HE-5 (induced by source paper "optimized skeleton based action recognition via sparsified graph regression"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "dynamic graph cnn for learning on point clouds"
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"
    - "sentence state lstm for text representation"
    - "spatio temporal graph convolution for skeleton based action recognition"
    - "cross sentence n ary relation extraction with graph lstms"
    - "conversation modeling on reddit using a graph structured lstm"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "ensemble one dimensional convolution neural networks for skeleton based action recognition"):
  with members:
    - "skeleton based action recognition using translation scale invariant image mapping and multi scale deep cnn"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "skeleton based action recognition using spatio temporal lstm network with trust gates"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "joint distance maps based action recognition with convolutional neural networks"
    - "skepxels spatio temporal image representation of human skeleton joints for action recognition"
    - "view adaptive recurrent neural networks for high performance human action recognition from skeleton data"

Connected through hyperedge HE-7 (induced by source paper "skeletal movement to color map a novel representation for 3d action recognition with inception residual networks"):
  with members:
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "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"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "rethinking the inception architecture for computer vision"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

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)