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: "classifying and visualizing motion capture sequences using deep neural networks"
  Abstract: The gesture recognition using motion capture data and depth sensors has recently drawn more attention in vision recognition. Currently most systems only classify dataset with a couple of dozens different actions. Moreover, feature extraction from the data is often computational complex. In this paper, we propose a novel system to recognize the actions from skeleton data with simple, but effective, features using deep neural networks. Features are extracted for each frame based on the relative positions of joints (PO), temporal differences (TD), and normalized trajectories of motion (NT). Given these features a hybrid multi-layer perceptron is trained, which simultaneously classifies and reconstructs input data. We use deep autoencoder to visualize learnt features, and the experiments show that deep neural networks can capture more discriminative information than, for instance, principal component analysis can. We test our system on a public database with 65 classes and more than 2,000 motion sequences. We obtain an accuracy above 95% which is, to our knowledge, the state of the art result for such a large dataset.

Connected through hyperedge HE-1 (induced by source paper "recent advances in deep learning an overview"):
  with members:
    - "deep learning a critical appraisal"
    - "adversarial attacks on neural network policies"
    - "deep learning for environmentally robust speech recognition an overview of recent developments"
    - "lstm a search space odyssey"
    - "a knowledge grounded neural conversation model"
    - "me r cnn multi expert region based cnn for object detection"
    - "on the origin of deep learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-2 (induced by source paper "a survey on trajectory clustering analysis"):
  with members:
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "globally continuous and non markovian crowd activity analysis from videos"
    - "a robust and efficient video representation for action recognition"
    - "learning to segment moving objects in videos"
    - "a gesture recognition system for detecting behavioral patterns of adhd"
    - "integrating graph partitioning and matching for trajectory analysis in video surveillance"
    - "ganc greedy agglomerative normalized cut"
    - "discovery of convoys in trajectory databases"

Connected through hyperedge HE-3 (induced by source paper "scalable and compact 3d action recognition with approximated rbf kernel machines"):
  with members:
    - "a compact kernel approximation for 3d action recognition"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "joint distance maps based action recognition with convolutional neural networks"
    - "kernelized covariance 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"
    - "action recognition based on joint trajectory maps using convolutional neural networks"
    - "deep learning on lie groups for skeleton based action recognition"

Connected through hyperedge HE-4 (induced by source paper "cur decompositions similarity matrices and subspace clustering"):
  with members:
    - "joint active learning with feature selection via cur matrix decomposition"
    - "filtrated spectral algebraic subspace clustering"
    - "filtrated algebraic subspace clustering"
    - "optimal cur matrix decompositions"
    - "cur algorithm for partially observed matrices"
    - "robust recovery of subspace structures by low rank representation"
    - "generalized principal component analysis gpca"
    - "hybrid linear modeling via local best fit flats"

Connected through hyperedge HE-5 (induced by source paper "semantic noise modeling for better representation learning"):
  with members:
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "neural machine translation by jointly learning to align and translate"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "a compact kernel approximation for 3d action recognition"):
  with members:
    - "kernelized covariance for action recognition"
    - "tensor representations via kernel linearization for action recognition from 3d skeletons"
    - "bregman divergences for infinite dimensional covariance matrices"
    - "random feature maps for dot product kernels"

Connected through hyperedge HE-7 (induced by source paper "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"):
  with members:
    - "going deeper with convolutions"
    - "recurrent neural network regularization"
    - "dropout improves recurrent neural networks for handwriting recognition"
    - "speech recognition with deep recurrent neural networks"
    - "unstructured human activity detection from rgbd images"

Connected through hyperedge HE-8 (induced by source paper "semi supervised learning with encoder decoder recurrent neural networks experiments with motion capture sequences"):
  with members:
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"
    - "stacked generative adversarial networks"
    - "an end to end spatio temporal attention model for human action recognition from skeleton data"
    - "generative adversarial text to image synthesis"
    - "contextual rnn gans for abstract reasoning diagram generation"
    - "theano a python framework for fast computation of mathematical expressions"

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)