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: "ensemble one dimensional convolution neural networks for skeleton based action recognition"
  Abstract: This letter proposes an ensemble neural network (Ensem-NN) for skeleton-based action recognition. The Ensem-NN is introduced based on the idea of ensemble learning, “two heads are better than one.” According to the property of skeleton sequences, we design one-dimensional convolution neural network with residual structure as  Base-Net . From entirety to local, from focus to motion, we designed four different subnets based on the  Base-Net  to extract diverse features. The first subnet is a  Two-stream Entirety Net  , which performs on the entirety skeleton and explores both temporal and spatial features. The second is a  Body-part Net , which can extract fine-grained spatial and temporal features. The third is an   Attention Net , in which a channel-wised attention mechanism can learn important frames and feature channels.  Frame-difference Net , as the fourth subnet, aims at exploring motion features. Finally, the four subnets are fused as one ensemble network. Experimental results show that the proposed Ensem-NN performs better than state-of-the-art methods on three widely used datasets.

Connected through hyperedge HE-1 (induced by source paper "a survey on 3d skeleton based action recognition using learning method"):
  with members:
    - "semantics guided neural networks for efficient skeleton based human action recognition"
    - "richlt activated graph convolutional network for action recognition with incomplete skeletons"
    - "make skeleton based action recognition model smaller faster and better"
    - "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"
    - "a comparative review of recent kinect based action recognition algorithms"
    - "skelemotion a new representation of skeleton joint sequences based on motion information for 3d action recognition"

Connected through hyperedge HE-2 (induced by source paper "etri activity3d a large scale rgb d dataset for robots to recognize daily activities of the elderly"):
  with members:
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "glimpse clouds human activity recognition from unstructured feature points"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "neural networks with activation networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "two stream rnn cnn for action recognition in 3d videos"
    - "cooperative training of deep aggregation networks for rgb d action recognition"

Connected through hyperedge HE-3 (induced by source paper "mc ista net adaptive measurement and initialization and channel attention optimization inspired neural network for compressive sensing"):
  with members:
    - "dr2 net deep residual reconstruction network for image compressive sensing"
    - "image super resolution using very deep residual channel attention networks"
    - "amp inspired deep networks for sparse linear inverse problems"
    - "learning deep cnn denoiser prior for image restoration"
    - "ista net iterative shrinkage thresholding algorithm inspired deep network for image compressive sensing"
    - "denoising based vector approximate message passing"
    - "deep residual learning for image recognition"
    - "new constructions of rip matrices with fast multiplication and fewer rows"

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)