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: "cooperative training of deep aggregation networks for rgb d action recognition"
  Abstract: A novel deep neural network training paradigm that exploits the conjoint information in multiple heterogeneous sources is proposed. Specifically, in a RGB-D based action recognition task, it cooperatively trains a single convolutional neural network (named c-ConvNet) on both RGB visual features and depth features, and deeply aggregates the two kinds of features for action recognition. Differently from the conventional ConvNet that learns the deep separable features for homogeneous modality-based classification with only one softmax loss function, the c-ConvNet enhances the discriminative power of the deeply learned features and weakens the undesired modality discrepancy by jointly optimizing a ranking loss and a softmax loss for both homogeneous and heterogeneous modalities. The ranking loss consists of intra-modality and cross-modality triplet losses, and it reduces both the intra-modality and cross-modality feature variations. Furthermore, the correlations between RGB and depth data are embedded in the c-ConvNet, and can be retrieved by either of the modalities and contribute to the recognition in the case even only one of the modalities is available. The proposed method was extensively evaluated on two large RGB-D action recognition datasets, ChaLearn LAP IsoGD and NTU RGB+D datasets, and one small dataset, SYSU 3D HOI, and achieved state-of-the-art results.

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 "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"
    - "ensemble one dimensional convolution neural networks for skeleton based action recognition"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "casia surf cefa a benchmark for multi modal cross ethnicity face anti spoofing"):
  with members:
    - "casia surf a large scale multi modal benchmark for face anti spoofing"
    - "face alignment in full pose range a 3d total solution"
    - "face de spoofing anti spoofing via noise modeling"
    - "exploiting temporal and depth information for multi frame face anti spoofing"
    - "a dataset and benchmark for large scale multi modal face anti spoofing"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "learning deep models for face anti spoofing binary or auxiliary supervision"
    - "racial faces in the wild reducing racial bias by information maximization adaptation network"

Connected through hyperedge HE-4 (induced by source paper "action recognition using volumetric motion representations"):
  with members:
    - "glimpse clouds human activity recognition from unstructured feature points"
    - "unsupervised learning of view invariant action representations"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "two stream rnn cnn for action recognition in 3d videos"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "unsupervised learning of long term motion dynamics for videos"
    - "chained multi stream networks exploiting pose motion and appearance for action classification and detection"
    - "scene flow to action map a new representation for rgb d based action recognition with convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "chalearn looking at people isogd and congd large scale rgb d gesture recognition"):
  with members:
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "rank pooling for action recognition"
    - "hand keypoint detection in single images using multiview bootstrapping"
    - "automatic recognition of facial displays of unfelt emotions"
    - "scene flow to action map a new representation for rgb d based action recognition with convolutional neural networks"
    - "convolutional pose machines"
    - "large scale continuous gesture recognition using convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "static and dynamic fusion for multi modal cross ethnicity face anti spoofing"):
  with members:
    - "casia surf a large scale multi modal benchmark for face anti spoofing"
    - "deep tree learning for zero shot face anti spoofing"
    - "face alignment in full pose range a 3d total solution"
    - "feathernets convolutional neural networks as light as feather for face anti spoofing"
    - "exploiting temporal and depth information for multi frame face anti spoofing"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "learning deep models for face anti spoofing binary or auxiliary supervision"
    - "rank pooling for action recognition"

Connected through hyperedge HE-7 (induced by source paper "rgb d based action recognition with light weight 3d convolutional networks"):
  with members:
    - "eco efficient convolutional network for online video understanding"
    - "a fine to coarse convolutional neural network for 3d human action recognition"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "action recognition for depth video using multi view dynamic images"
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"
    - "modality distillation with multiple stream networks for action recognition"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-8 (induced by source paper "depth pooling based large scale 3d action recognition with convolutional neural networks"):
  with members:
    - "scene flow to action map a new representation for rgb d based action recognition with convolutional neural networks"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "large scale continuous gesture recognition using convolutional neural networks"
    - "spatio temporal lstm with trust gates for 3d human action recognition"
    - "action recognition based on joint trajectory maps using convolutional neural networks"
    - "going deeper into action recognition a survey"
    - "differential recurrent neural networks for action recognition"
    - "slow and steady feature analysis higher order temporal coherence in video"

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)