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: "a novel approach for robust multi human action detection and recognition based on 3 dimentional convolutional neural networks"
  Abstract: In recent years, various attempts have been proposed to explore the use of spatial and temporal information for human action recognition using convolutional neural networks (CNNs). However, only a small number of methods are available for the recognition of many human actions performed by more than one person in the same surveillance video. This paper proposes a novel technique for multiple human action recognition using a new architecture based on 3Dimdenisional deep learning with application to video surveillance systems. The first stage of the model uses a new representation of the data by extracting the sequence of each person acting in the scene. An analysis of each sequence to detect the corresponding actions is also proposed. KTH, Weizmann and UCF-ARG datasets were used for training, new datasets were also constructed which include a number of persons having multiple actions were used for testing the proposed algorithm. The results of this work revealed that the proposed method provides more accurate multi human action recognition achieving 98%. Other videos were used for the evaluation including datasets (UCF101, Hollywood2, HDMB51, and YouTube) without any preprocessing and the results obtained suggest that our proposed method clearly improves the performances when compared to state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "image inpainting a review"):
  with members:
    - "local deep feature alignment for unsupervised dimension reduction"
    - "blind visual motif removal from a single image"
    - "adversarial inpainting of medical image modalities"
    - "deep hyperspectral prior denoising inpainting super resolution"
    - "pepsi fast and lightweight network for image inpainting"
    - "learning pyramid context encoder network for high quality image inpainting"
    - "foreground aware image inpainting"
    - "coherent semantic attention for image inpainting"

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)