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: "relational network for skeleton based action recognition"
  Abstract: With the fast development of effective and low-cost human skeleton capture systems, skeleton-based action recognition has attracted much attention recently. Most existing methods use Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) to extract spatio-temporal information embedded in the skeleton sequences for action recognition. However, these approaches are limited in the ability of relational modeling in a single skeleton, due to the loss of important structural information when converting the raw skeleton data to adapt to the input format of CNN or RNN. In this paper, we propose an Attentional Recurrent Relational Network-LSTM (ARRN-LSTM) to simultaneously model spatial configurations and temporal dynamics in skeletons for action recognition. We introduce the Recurrent Relational Network to learn the spatial features in a single skeleton, followed by a multi-layer LSTM to learn the temporal features in the skeleton sequences. Between the two modules, we design an adaptive attentional module to focus attention on the most discriminative parts in the single skeleton. To exploit the complementarity from different geometries in the skeleton for sufficient relational modeling, we design a two-stream architecture to learn the structural features among joints and lines simultaneously. Extensive experiments are conducted on several popular skeleton datasets and the results show that the proposed approach achieves better results than most mainstream methods.

Connected through hyperedge HE-1 (induced by source paper "centrality graph convolutional networks for skeleton based action recognition"):
  with members:
    - "learning class regularized features for action recognition"
    - "semantics guided neural networks for efficient skeleton based human action recognition"
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "real world anomaly detection in surveillance videos"
    - "sports camera calibration via synthetic data"
    - "pose flow efficient online pose tracking"

Connected through hyperedge HE-2 (induced by source paper "skeleton based action recognition with multi stream adaptive graph convolutional networks"):
  with members:
    - "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"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "glimpse clouds human activity recognition from unstructured feature points"
    - "skeleton based relational modeling for action recognition"
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"
    - "videos as space time region graphs"

Connected through hyperedge HE-3 (induced by source paper "spatio temporal pyramid graph convolutions for human action recognition and postural assessment"):
  with members:
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "skeleton based action recognition of people handling objects"
    - "in the wild human pose estimation using explicit 2d features and intermediate 3d representations"
    - "toward ergonomic risk prediction via segmentation of indoor object manipulation actions using spatiotemporal convolutional networks"
    - "unsupervised discovery of parts structure and dynamics"
    - "open set domain adaptation for image and action recognition"
    - "a comprehensive survey on graph neural networks"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"

Connected through hyperedge HE-4 (induced by source paper "two stream adaptive graph convolutional networks for skeleton based action recognition"):
  with members:
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "skeleton based relational modeling for action recognition"
    - "non local neural networks"
    - "skeleton based action recognition using translation scale invariant image mapping and multi scale deep cnn"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "two stream 3d convolutional neural network for skeleton based action recognition"
    - "the kinetics human action video dataset"

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)