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: "motion feature augmented recurrent neural network for skeleton based dynamic hand gesture recognition"
  Abstract: Dynamic hand gesture recognition has attracted increasing interests because of its importance for human computer interaction. In this paper, we propose a new motion feature augmented recurrent neural network for skeleton-based dynamic hand gesture recognition. Finger motion features are extracted to describe finger movements and global motion features are utilized to represent the global movement of hand skeleton. These motion features are then fed into a bidirectional recurrent neural network (RNN) along with the skeleton sequence, which can augment the motion features for RNN and improve the classification performance. Experiments demonstrate that our proposed method is effective and outperforms start-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "vision based body gesture meta features for affective computing"):
  with members:
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "topic modeling based multi modal depression detection"
    - "hand keypoint detection in single images using multiview bootstrapping"
    - "convolutional pose machines"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "cnn features off the shelf an astounding baseline for recognition"

Connected through hyperedge HE-2 (induced by source paper "pose guided structured region ensemble network for cascaded hand pose estimation"):
  with members:
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "intel realsense stereoscopic depth cameras"
    - "region ensemble network improving convolutional network for hand pose estimation"
    - "end to end global to local cnn learning for hand pose recovery in depth data"
    - "dynamics based 3d skeletal hand tracking"
    - "mask r cnn"
    - "training a feedback loop for hand pose estimation"
    - "robust 3d hand pose estimation in single depth images from single view cnn to multi view cnns"

Connected through hyperedge HE-3 (induced by source paper "a neural network based on spd manifold learning for skeleton based hand gesture recognition"):
  with members:
    - "spatio temporal graph convolution for skeleton based action recognition"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"
    - "deepkspd learning kernel matrix based spd representation for fine grained image recognition"
    - "is second order information helpful for large scale visual recognition"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "first person hand action benchmark with rgb d videos and 3d hand pose annotations"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "dimensionality reduction on spd manifolds the emergence of geometry aware methods"

Connected through hyperedge HE-4 (induced by source paper "construct dynamic graphs for hand gesture recognition via spatial temporal attention"):
  with members:
    - "semantic graph convolutional networks for 3d human pose regression"
    - "quantized densely connected u nets for efficient landmark localization"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "factual or emotional stylized image captioning with adaptive learning and attention"
    - "learning to forecast and refine residual motion for image to video generation"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "cr gan learning complete representations for multi view generation"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"

Connected through hyperedge HE-5 (induced by source paper "le hgr a lightweight and efficient rgb based online gesture recognition network for embedded ar devices"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "memory attention networks for skeleton based action recognition"
    - "pooling pyramid network for object detection"
    - "view adaptive neural networks for high performance skeleton based human action recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "skeleton based action recognition using spatio temporal lstm network with trust gates"
    - "convolutional pose machines"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-6 (induced by source paper "skeleton based hand gesture recognition by learning spd matrices with neural networks"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "graph edge convolutional neural networks for skeleton based action recognition"
    - "spatio temporal graph convolution for skeleton based action recognition"
    - "feastnet feature steered graph convolutions for 3d shape analysis"
    - "deepkspd learning kernel matrix based spd representation for fine grained image recognition"
    - "is second order information helpful for large scale visual recognition"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "building deep networks on grassmann manifolds"

Connected through hyperedge HE-7 (induced by source paper "deepgru deep gesture recognition utility"):
  with members:
    - "exploiting recurrent neural networks and leap motion controller for the recognition of sign language and semaphoric hand gestures"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "glimpse clouds human activity recognition from unstructured feature points"
    - "cnn based action recognition and supervised domain adaptation on 3d body skeletons via kernel feature maps"
    - "non linear temporal subspace representations for activity recognition"
    - "sts classification with dual stream cnn"
    - "skeleton based human action recognition with global context aware attention lstm networks"
    - "excitation backprop for rnns"

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)