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: "deepgru deep gesture recognition utility"
  Abstract: We propose DeepGRU, a novel end-to-end deep network model informed by recent developments in deep learning for gesture and action recognition, that is streamlined and device-agnostic. DeepGRU, which uses only raw skeleton, pose or vector data is quickly understood, implemented, and trained, and yet achieves state-of-the-art results on challenging datasets. At the heart of our method lies a set of stacked gated recurrent units (GRU), two fully-connected layers and a novel global attention model. We evaluate our method on seven publicly available datasets, containing various number of samples and spanning over a broad range of interactions (full-body, multi-actor, hand gestures, etc.). In all but one case we outperform the state-of-the-art pose-based methods. For instance, we achieve a recognition accuracy of 84.9% and 92.3% on cross-subject and cross-view tests of the NTU RGB+D dataset respectively, and also 100% recognition accuracy on the UT-Kinect dataset. While DeepGRU works well on large datasets with many training samples, we show that even in the absence of a large number of training data, and with as little as four samples per class, DeepGRU can beat traditional methods specifically designed for small training sets. Lastly, we demonstrate that even without powerful hardware, and using only the CPU, our method can still be trained in under 10 minutes on small-scale datasets, making it an enticing choice for rapid application prototyping and development.

Connected through hyperedge HE-1 (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-2 (induced by source paper "totally deep support vector machines"):
  with members:
    - "graph neural networks with convolutional arma filters"
    - "simplifying graph convolutional networks"
    - "a novel geometric framework on gram matrix trajectories for human behavior understanding"
    - "skeleton based human action recognition with global context aware attention lstm networks"
    - "pose conditioned spatio temporal attention for human action recognition"
    - "view adaptive recurrent neural networks for high performance human action recognition from skeleton data"
    - "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"

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)