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: "skeletal movement to color map a novel representation for 3d action recognition with inception residual networks"
  Abstract: We propose a novel skeleton-based representation for 3D action recognition in videos using Deep Convolutional Neural Networks (D-CNNs). Two key issues have been addressed: First, how to construct a robust representation that easily captures the spatial-temporal evolutions of motions from skeleton sequences. Second, how to design D-CNNs capable of learning discriminative features from the new representation in a effective manner. To address these tasks, a skeletonbased representation, namely, SPMF (Skeleton Pose-Motion Feature) is proposed. The SPMFs are built from two of the most important properties of a human action: postures and their motions. Therefore, they are able to effectively represent complex actions. For learning and recognition tasks, we design and optimize new D-CNNs based on the idea of Inception Residual networks to predict actions from SPMFs. Our method is evaluated on two challenging datasets including MSR Action3D and NTU-RGB+D. Experimental results indicated that the proposed method surpasses state-of-the-art methods whilst requiring less computation.

Connected through hyperedge HE-1 (induced by source paper "a unified deep framework for joint 3d pose estimation and action recognition from a single rgb camera"):
  with members:
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "view adaptive neural networks for high performance skeleton based human action recognition"
    - "cnn based action recognition and supervised domain adaptation on 3d body skeletons via kernel feature maps"
    - "efficient neural architecture search via parameter sharing"
    - "exploiting deep residual networks for human action recognition from skeletal data"
    - "skeleton based human action recognition with global context aware attention lstm networks"
    - "self normalizing neural networks"

Connected through hyperedge HE-2 (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"
    - "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"
    - "spatio temporal lstm with trust gates for 3d human action recognition"

Connected through hyperedge HE-3 (induced by source paper "skeleton based activity recognition by fusing part wise spatio temporal and attention driven residues"):
  with members:
    - "simple yet efficient real time pose based action recognition"
    - "exploiting deep residual networks for human action recognition from skeletal data"
    - "attention is all you need"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "massive exploration of neural machine translation architectures"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"
    - "an end to end spatio temporal attention model for human action recognition from skeleton data"

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)