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: "skeleton based action recognition using spatio temporal lstm network with trust gates"
  Abstract: Skeleton-based human action recognition has attracted a lot of research attention during the past few years. Recent works attempted to utilize recurrent neural networks to model the temporal dependencies between the 3D positional configurations of human body joints for better analysis of human activities in the skeletal data. The proposed work extends this idea to spatial domain as well as temporal domain to better analyze the hidden sources of action-related information within the human skeleton sequences in both of these domains simultaneously. Based on the pictorial structure of Kinect's skeletal data, an effective tree-structure based traversal framework is also proposed. In order to deal with the noise in the skeletal data, a new gating mechanism within LSTM module is introduced, with which the network can learn the reliability of the sequential data and accordingly adjust the effect of the input data on the updating procedure of the long-term context representation stored in the unit's memory cell. Moreover, we introduce a novel multi-modal feature fusion strategy within the LSTM unit in this paper. The comprehensive experimental results on seven challenging benchmark datasets for human action recognition demonstrate the effectiveness of the proposed method.

Connected through hyperedge HE-1 (induced by source paper "feedback graph convolutional network for skeleton based action recognition"):
  with members:
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "feedback network for image super resolution"
    - "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"
    - "skeleton image representation for 3d action recognition based on tree structure and reference joints"
    - "interaction relational network for mutual action recognition"
    - "vertex feature encoding and hierarchical temporal modeling in a spatial temporal graph convolutional network for action recognition"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"

Connected through hyperedge HE-2 (induced by source paper "finehand learning hand shapes for american sign language recognition"):
  with members:
    - "sign language recognition analysis using multimodal data"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "pose flow efficient online pose tracking"
    - "skeleton based human action recognition with global context aware attention lstm networks"
    - "towards accurate multi person pose estimation in the wild"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "skepxels spatio temporal image representation of human skeleton joints for action recognition"
    - "convolutional pose machines"

Connected through hyperedge HE-3 (induced by source paper "deep independently recurrent neural network indrnn"):
  with members:
    - "temporal transformer networks joint learning of invariant and discriminative time warping"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "part based graph convolutional network for action recognition"
    - "learning long term dependencies via fourier recurrent units"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "view adaptive neural networks for high performance skeleton based human action recognition"
    - "simple recurrent units for highly parallelizable recurrence"
    - "lstm a search space odyssey"

Connected through hyperedge HE-4 (induced by source paper "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"):
  with members:
    - "feature boosting network for 3d pose estimation"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "glimpse clouds human activity recognition from unstructured feature points"
    - "video representation learning using discriminative pooling"
    - "learning and refining of privileged information based rnns for action recognition from depth sequences"
    - "sphereface deep hypersphere embedding for face recognition"
    - "when kernel methods meet feature learning log covariance network for action recognition from skeletal data"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"

Connected through hyperedge HE-5 (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-6 (induced by source paper "a comparative review of recent kinect based action recognition algorithms"):
  with members:
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "two stream adaptive graph convolutional networks for skeleton based action recognition"
    - "cnn based action recognition and supervised domain adaptation on 3d body skeletons via kernel feature maps"
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"
    - "spatio temporal graph convolution for skeleton based action recognition"

Connected through hyperedge HE-7 (induced by source paper "discriminative video representation learning using support vector classifiers"):
  with members:
    - "moments in time dataset one million videos for event understanding"
    - "a short note about kinetics 600"
    - "non linear temporal subspace representations for activity recognition"
    - "video representation learning using discriminative pooling"
    - "higher order pooling of cnn features via kernel linearization for action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "generalized rank pooling for activity recognition"
    - "appearance and relation networks for video classification"

Connected through hyperedge HE-8 (induced by source paper "skeleton based online action prediction using scale selection network"):
  with members:
    - "leveraging structural context models and ranking score fusion for human interaction prediction"
    - "skeleton based human action recognition with global context aware attention lstm networks"
    - "skeleton boxes solving skeleton based action detection with a single deep convolutional neural network"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "temporal action detection with structured segment networks"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "cascaded boundary regression for temporal action detection"
    - "temporal context network for activity localization in videos"

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)