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: "rgb d based human motion recognition with deep learning a survey"
  Abstract: Human motion recognition is one of the most important branches of human-centered research activities. In recent years, motion recognition based on RGB-D data has attracted much attention. Along with the development in artificial intelligence, deep learning techniques have gained remarkable success in computer vision. In particular, convolutional neural networks (CNN) have achieved great success for image-based tasks, and recurrent neural networks (RNN) are renowned for sequence-based problems. Specifically, deep learning methods based on the CNN and RNN architectures have been adopted for motion recognition using RGB-D data. In this paper, a detailed overview of recent advances in RGB-D-based motion recognition is presented. The reviewed methods are broadly categorized into four groups, depending on the modality adopted for recognition: RGB-based, depth-based, skeleton-based and multi-modal-based. As a survey focused on the application of deep learning to RGB-D-based motion recognition, we explicitly discuss the advantages and limitations of existing techniques. Particularly, we highlighted the methods of encoding spatial-temporal-structural information inherent in video sequence, and discuss potential directions for future research.

Connected through hyperedge HE-1 (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-2 (induced by source paper "semantics guided neural networks for efficient skeleton based human action recognition"):
  with members:
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "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"
    - "eleatt rnn adding attentiveness to neurons in recurrent neural networks"
    - "deep high resolution representation learning for human pose estimation"
    - "learning deep bilinear transformation for fine grained image representation"
    - "bag of tricks for image classification with convolutional neural networks"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"

Connected through hyperedge HE-3 (induced by source paper "meth a family of high resolution and variable shape image challenges"):
  with members:
    - "leveraging the bfloat16 artificial intelligence datatype for higher precision computations"
    - "a study of bfloat16 for deep learning training"
    - "continual lifelong learning with neural networks a review"
    - "benchmarking tpu gpu and cpu platforms for deep learning"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "exascale deep learning for climate analytics"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "high resolution breast cancer screening with multi view deep convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "an attention enhanced graph convolutional lstm network for skeleton based action recognition"):
  with members:
    - "part based graph convolutional network for action recognition"
    - "action recognition with spatio temporal visual attention on skeleton image sequences"
    - "memory attention networks for skeleton based action recognition"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "learning human object interactions by graph parsing neural networks"
    - "how powerful are graph neural networks"
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"
    - "situation recognition with graph neural networks"

Connected through hyperedge HE-5 (induced by source paper "dmcl distillation multiple choice learning for multimodal action recognition"):
  with members:
    - "unifying heterogeneous classifiers with distillation"
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "learning with privileged information via adversarial discriminative modality distillation"
    - "human action recognition and prediction a survey"
    - "snapshot distillation teacher student optimization in one generation"
    - "action recognition for depth video using multi view dynamic images"
    - "motion feature network fixed motion filter for action recognition"
    - "modality distillation with multiple stream networks for action recognition"

Connected through hyperedge HE-6 (induced by source paper "being the center of attention a person context cnn framework for personality recognition"):
  with members:
    - "structural recurrent neural network srnn for group activity analysis"
    - "persemon a deep network for joint analysis of apparent personality emotion and their relationship"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "skepxels spatio temporal image representation of human skeleton joints for action recognition"
    - "social scene understanding end to end multi person action localization and collective activity recognition"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "salsa a novel dataset for multimodal group behavior analysis"
    - "learning deep features for discriminative localization"

Connected through hyperedge HE-7 (induced by source paper "spatio temporal fusion networks for action recognition"):
  with members:
    - "spatiotemporal pyramid network for video action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "optical flow guided feature a fast and robust motion representation for video action recognition"
    - "action recognition with coarse to fine deep feature integration and asynchronous fusion"
    - "temporal convolutional networks for action segmentation and detection"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-8 (induced by source paper "action recognition for depth video using multi view dynamic images"):
  with members:
    - "rank pooling for action recognition"
    - "a new representation of skeleton sequences for 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"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"
    - "deep learning on lie groups for skeleton based action recognition"
    - "action recognition with dynamic image networks"
    - "towards viewpoint invariant 3d human pose estimation"

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)