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: "a generative restricted boltzmann machine based method for high dimensional motion data modeling"
  Abstract: Extended RBM to model spatio-temporal patterns among high-dimensional motion data.Generative approach to perform classification using RBM, for both binary and multi-class classification.High classification accuracy in two computer vision applications: facial expression recognition and human action recognition. Many computer vision applications involve modeling complex spatio-temporal patterns in high-dimensional motion data. Recently, restricted Boltzmann machines (RBMs) have been widely used to capture and represent spatial patterns in a single image or temporal patterns in several time slices. To model global dynamics and local spatial interactions, we propose to theoretically extend the conventional RBMs by introducing another term in the energy function to explicitly model the local spatial interactions in the input data. A learning method is then proposed to perform efficient learning for the proposed model. We further introduce a new method for multi-class classification that can effectively estimate the infeasible partition functions of different RBMs such that RBM is treated as a generative model for classification purpose. The improved RBM model is evaluated on two computer vision applications: facial expression recognition and human action recognition. Experimental results on benchmark databases demonstrate the effectiveness of the proposed algorithm.

Connected through hyperedge HE-1 (induced by source paper "variational conditional dependence hidden markov models for human action recognition"):
  with members:
    - "nonparametric bayesian deep networks with local competition"
    - "action recognition based on joint trajectory maps using convolutional neural networks"
    - "on prediction using variable order markov models"

Connected through hyperedge HE-2 (induced by source paper "self learning local supervision encoding framework to constrict and disperse feature distribution for clustering"):
  with members:
    - "restricted boltzmann machines with gaussian visible units guided by pairwise constraints"
    - "label free supervision of neural networks with physics and domain knowledge"
    - "a novel feature extraction method for scene recognition based on centered convolutional restricted boltzmann machines"
    - "deep transductive semi supervised maximum margin clustering"
    - "learning structured ordinal measures for video based face recognition"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "scene flow to action map a new representation for rgb d based action recognition with convolutional neural networks"):
  with members:
    - "large scale isolated gesture recognition using convolutional neural networks"
    - "rank pooling for action recognition"
    - "joint distance maps 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"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"
    - "action recognition based on joint trajectory maps using convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "skeleton based action recognition using translation scale invariant image mapping and multi scale deep cnn"):
  with members:
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"
    - "action recognition based on joint trajectory maps using convolutional neural networks"
    - "an end to end spatio temporal attention model for human action recognition from skeleton data"
    - "learning a pose lexicon for semantic action recognition"
    - "sparseness meets deepness 3d human pose estimation from monocular video"
    - "deep residual learning for image recognition"
    - "differential recurrent neural networks for action recognition"

Connected through hyperedge HE-5 (induced by source paper "cooperative training of deep aggregation networks for rgb d action recognition"):
  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"
    - "deep multimodal feature analysis for action recognition in rgb d videos"
    - "rgb d based action recognition datasets a survey"
    - "facenet a unified embedding for face recognition and clustering"
    - "slow and steady feature analysis higher order temporal coherence in video"
    - "very deep convolutional networks for large scale image recognition"
    - "long term recurrent convolutional networks for visual recognition and description"

Connected through hyperedge HE-6 (induced by source paper "restricted boltzmann machines with gaussian visible units guided by pairwise constraints"):
  with members:
    - "gaussian binary restricted boltzmann machines for modeling natural image statistics"
    - "estimating 3d trajectories from 2d projections via disjunctive factored four way conditional restricted boltzmann machines"
    - "theta rbm unfactored gated restricted boltzmann machine for rotation invariant representations"
    - "a novel feature extraction method for scene recognition based on centered convolutional restricted boltzmann machines"
    - "deep transductive semi supervised maximum margin clustering"
    - "speech recognition with deep recurrent neural networks"
    - "modeling documents with deep boltzmann machines"
    - "conditional restricted boltzmann machines for structured output prediction"

Connected through hyperedge HE-7 (induced by source paper "action recognition based on joint trajectory maps using convolutional neural networks"):
  with members:
    - "rgb d based human motion recognition with deep learning a survey"
    - "rank pooling for 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"
    - "temporal segment networks towards good practices for deep action recognition"
    - "learning a pose lexicon for semantic action recognition"
    - "beyond short snippets deep networks for video classification"

Connected through hyperedge HE-8 (induced by source paper "ensemble of hankel matrices for face emotion recognition"):
  with members:
    - "using hankel matrices for dynamics based facial emotion recognition and pain detection"
    - "emotional expression classification using time series kernels"
    - "compressive acquisition of dynamic scenes"
    - "a kernel for time series based on global alignments"

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)