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: "space time representation of people based on 3d skeletal data a review"
  Abstract: Spatiotemporal human representation based on 3D visual perception data is a rapidly growing research area. Based on the information sources, these representations can be broadly categorized into two groups based on RGB-D information or 3D skeleton data. Recently, skeleton-based human representations have been intensively studied and kept attracting an increasing attention, due to their robustness to variations of viewpoint, human body scale and motion speed as well as the realtime, online performance. This paper presents a comprehensive survey of existing space-time representations of people based on 3D skeletal data, and provides an informative categorization and analysis of these methods from the perspectives, including information modality, representation encoding, structure and transition, and feature engineering. We also provide a brief overview of skeleton acquisition devices and construction methods, enlist a number of public benchmark datasets with skeleton data, and discuss potential future research directions.

Connected through hyperedge HE-1 (induced by source paper "a fine to coarse convolutional neural network for 3d human action recognition"):
  with members:
    - "skeleton based human action recognition with global context aware attention lstm networks"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "skeleton based action recognition using spatio temporal lstm network with trust gates"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "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"
    - "an end to end spatio temporal attention model for human action recognition from skeleton data"
    - "learning transferable features with deep adaptation networks"

Connected through hyperedge HE-2 (induced by source paper "simultaneous joint and object trajectory templates for human activity recognition from 3 d data"):
  with members:
    - "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"
    - "learning human activities and object affordances from rgb d videos"
    - "unstructured human activity detection from rgbd images"

Connected through hyperedge HE-3 (induced by source paper "a new representation of skeleton sequences for 3d action recognition"):
  with members:
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "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"
    - "an empirical study of language cnn for image captioning"
    - "tensor representations via kernel linearization for action recognition from 3d skeletons"
    - "temporal segment networks towards good practices for deep action recognition"
    - "cnn image retrieval learns from bow unsupervised fine tuning with hard examples"

Connected through hyperedge HE-4 (induced by source paper "learning human motion models for long term predictions"):
  with members:
    - "deep representation learning for human motion prediction and classification"
    - "anticipating many futures online human motion prediction and synthesis for human robot collaboration"
    - "on human motion prediction using 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"
    - "structured prediction of 3d human pose with deep neural networks"
    - "differential recurrent neural networks for action recognition"
    - "show and tell a neural image caption generator"

Connected through hyperedge HE-5 (induced by source paper "deep representation learning for human motion prediction and classification"):
  with members:
    - "structural rnn deep learning on spatio temporal graphs"
    - "recurrent network models for human dynamics"
    - "factors of transferability for a generic convnet representation"
    - "caffe convolutional architecture for fast feature embedding"
    - "visualizing and understanding convolutional networks"
    - "what regularized auto encoders learn from the data generating distribution"
    - "representation learning a review and new perspectives"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "view adaptive recurrent neural networks for high performance human action recognition from skeleton data"):
  with members:
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "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"
    - "an end to end spatio temporal attention model for human action recognition from skeleton data"
    - "theano a python framework for fast computation of mathematical expressions"
    - "adam a method for stochastic optimization"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-7 (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"
    - "a hierarchical pose based approach to complex action understanding using dictionaries of actionlets and motion poselets"

Connected through hyperedge HE-8 (induced by source paper "deep multimodal feature analysis for action recognition in rgb d videos"):
  with members:
    - "skeleton based action recognition using spatio temporal lstm network with trust gates"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "learning a deep model for human action recognition from novel viewpoints"
    - "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"
    - "beyond short snippets deep networks for video classification"
    - "differential recurrent neural networks for action recognition"
    - "action recognition with trajectory pooled deep convolutional descriptors"

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)