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: "mining mid level features for action recognition based on effective skeleton representation"
  Abstract: Recently, mid-level features have shown promising performance in computer vision. Mid-level features learned by incorporating class-level information are potentially more discriminative than traditional low-level local features. In this paper, an effective method is proposed to extract mid-level features from Kinect skeletons for 3D human action recognition. Firstly, the orientations of limbs connected by two skeleton joints are computed and each orientation is encoded into one of the 27 states indicating the spatial relationship of the joints. Secondly, limbs are combined into parts and the limb's states are mapped into part states. Finally, frequent pattern mining is employed to mine the most frequent and relevant (discriminative, representative and non-redundant) states of parts in continuous several frames. These parts are referred to as Frequent Local Parts or FLPs. The FLPs allow us to build powerful bag-of-FLP-based action representation. This new representation yields state-of-the-art results on MSR DailyActivity3D and MSR ActionPairs3D.

Connected through hyperedge HE-1 (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"
    - "investigation of different skeleton features for cnn based 3d action recognition"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"

Connected through hyperedge HE-2 (induced by source paper "skelemotion a new representation of skeleton joint sequences based on motion information for 3d action recognition"):
  with members:
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "action recognition with spatio temporal visual attention on skeleton image sequences"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "skeleton based human action recognition with global context aware attention lstm networks"
    - "activity recognition based on a magnitude orientation stream network"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "skeleton image representation for 3d action recognition based on tree structure and reference joints"):
  with members:
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "skeleton based human action recognition with global context aware attention lstm 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"
    - "spatio temporal lstm with trust gates for 3d human action recognition"

Connected through hyperedge HE-4 (induced by source paper "skeleton based human action recognition with global context aware attention lstm networks"):
  with members:
    - "leveraging structural context models and ranking score fusion for human interaction prediction"
    - "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"
    - "action recognition based on joint trajectory maps using convolutional neural networks"
    - "an end to end spatio temporal attention model for human action recognition from skeleton data"
    - "online human action detection using joint classification regression recurrent neural networks"

Connected through hyperedge HE-5 (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-6 (induced by source paper "large scale isolated gesture recognition using convolutional neural networks"):
  with members:
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "large scale continuous gesture recognition using convolutional neural networks"
    - "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"
    - "beyond short snippets deep networks for video classification"
    - "differential recurrent neural networks for action recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "long term recurrent convolutional networks for visual recognition and description"

Connected through hyperedge HE-7 (induced by source paper "joint distance maps based action recognition with convolutional neural networks"):
  with members:
    - "r c3d region convolutional 3d network for temporal activity detection"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "view adaptive recurrent neural networks for high performance human action recognition from skeleton data"
    - "scene flow to action map a new representation for rgb d based action recognition with convolutional neural networks"
    - "pku mmd a large scale benchmark for continuous multi modal human action understanding"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "robust 3d action recognition through sampling local appearances and global distributions"):
  with members:
    - "elastic functional coding of riemannian trajectories"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "differential recurrent neural networks for action recognition"
    - "histogram of oriented principal components for cross view action recognition"
    - "a discussion on the validation tests employed to compare human action recognition methods using the msr action3d dataset"
    - "learning human activities and object affordances from rgb d videos"
    - "unstructured human activity detection from rgbd images"

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)