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: "view adaptive recurrent neural networks for high performance human action recognition from skeleton data"
  Abstract: Skeleton-based human action recognition has recently attracted increasing attention due to the popularity of 3D skeleton data. One main challenge lies in the large view variations in captured human actions. We propose a novel view adaptation scheme to automatically regulate observation viewpoints during the occurrence of an action. Rather than re-positioning the skeletons based on a human defined prior criterion, we design a view adaptive recurrent neural network (RNN) with LSTM architecture, which enables the network itself to adapt to the most suitable observation viewpoints from end to end. Extensive experiment analyses show that the proposed view adaptive RNN model strives to (1) transform the skeletons of various views to much more consistent viewpoints and (2) maintain the continuity of the action rather than transforming every frame to the same position with the same body orientation. Our model achieves state-of-the-art performance on three benchmark datasets. On the current largest NTU RGB+D dataset, our scheme outperforms the state of the art by an impressive 6% gain in accuracy.

Connected through hyperedge HE-1 (induced by source paper "centrality graph convolutional networks for skeleton based action recognition"):
  with members:
    - "learning class regularized features for action recognition"
    - "semantics guided neural networks for efficient skeleton based human action recognition"
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "real world anomaly detection in surveillance videos"
    - "sports camera calibration via synthetic data"
    - "pose flow efficient online pose tracking"

Connected through hyperedge HE-2 (induced by source paper "infrared and 3d skeleton feature fusion for rgb d action recognition"):
  with members:
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "two stream adaptive graph convolutional networks for skeleton based action recognition"
    - "view adaptive neural networks for high performance skeleton based human action recognition"
    - "how powerful are graph neural networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "intel realsense stereoscopic depth cameras"

Connected through hyperedge HE-3 (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-4 (induced by source paper "modality compensation network cross modal adaptation for action recognition"):
  with members:
    - "spatiotemporal pyramid network for video action recognition"
    - "temporal bilinear networks for video action recognition"
    - "learning and refining of privileged information based rnns for action recognition from depth sequences"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "chained multi stream networks exploiting pose motion and appearance for action classification and detection"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "feedback graph convolutional network for skeleton based action recognition"):
  with members:
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "feedback network for image super resolution"
    - "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"
    - "interaction relational network for mutual action recognition"
    - "vertex feature encoding and hierarchical temporal modeling in a spatial temporal graph convolutional network for action recognition"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"

Connected through hyperedge HE-6 (induced by source paper "synthetic humans for action recognition from unseen viewpoints"):
  with members:
    - "temporally coherent full 3d mesh human pose recovery from monocular video"
    - "hierarchically learned view invariant representations for cross view action recognition"
    - "learning joint reconstruction of hands and manipulated objects"
    - "learning to train with synthetic humans"
    - "convolutional mesh regression for single image human shape reconstruction"
    - "sim2real transfer learning for 3d pose estimation motion to the rescue"
    - "virtualhome simulating household activities via programs"
    - "human action recognition and prediction a survey"

Connected through hyperedge HE-7 (induced by source paper "human action recognition with multi laplacian graph convolutional networks"):
  with members:
    - "graph neural networks with convolutional arma filters"
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "simplifying graph convolutional networks"
    - "graph wavelet neural network"
    - "graph convolutional neural networks via scattering"
    - "a comprehensive survey on graph neural networks"
    - "part based graph convolutional network for action recognition"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"

Connected through hyperedge HE-8 (induced by source paper "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"):
  with members:
    - "feature boosting network for 3d pose estimation"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "glimpse clouds human activity recognition from unstructured feature points"
    - "video representation learning using discriminative pooling"
    - "learning and refining of privileged information based rnns for action recognition from depth sequences"
    - "sphereface deep hypersphere embedding for face recognition"
    - "when kernel methods meet feature learning log covariance network for action recognition from skeletal data"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent 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)