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: "3d human activity recognition with reconfigurable convolutional neural networks"
  Abstract: Human activity understanding with 3D/depth sensors has received increasing attention in multimedia processing and interactions. This work targets on developing a novel deep model for automatic activity recognition from RGB-D videos. We represent each human activity as an ensemble of cubic-like video segments, and learn to discover the temporal structures for a category of activities, i.e. how the activities to be decomposed in terms of classification. Our model can be regarded as a structured deep architecture, as it extends the convolutional neural networks (CNNs) by incorporating structure alternatives. Specifically, we build the network consisting of 3D convolutions and max-pooling operators over the video segments, and introduce the latent variables in each convolutional layer manipulating the activation of neurons. Our model thus advances existing approaches in two aspects: (i) it acts directly on the raw inputs (grayscale-depth data) to conduct recognition instead of relying on hand-crafted features, and (ii) the model structure can be dynamically adjusted accounting for the temporal variations of human activities, i.e. the network configuration is allowed to be partially activated during inference. For model training, we propose an EM-type optimization method that iteratively (i) discovers the latent structure by determining the decomposed actions for each training example, and (ii) learns the network parameters by using the back-propagation algorithm. Our approach is validated in challenging scenarios, and outperforms state-of-the-art methods. A large human activity database of RGB-D videos is presented in addition.

Connected through hyperedge HE-1 (induced by source paper "convolutional neural networks for sentiment analysis in persian social media"):
  with members:
    - "enriching word vectors with subword information"
    - "a sensitivity analysis of and practitioners guide to convolutional neural networks for sentence classification"
    - "persian sentiment analyzer a framework based on a novel feature selection method"
    - "convolutional neural networks for sentence classification"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-2 (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-3 (induced by source paper "bonn activity maps dataset description"):
  with members:
    - "efficient 3d reconstruction and streaming for group scale multi client live telepresence"
    - "a dataset of multi illumination images in the wild"
    - "the replica dataset a digital replica of indoor spaces"
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "latticenet fast point cloud segmentation using permutohedral lattices"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "joint 2d 3d semantic data for indoor scene understanding"

Connected through hyperedge HE-4 (induced by source paper "ev action electromyography vision multi modal action dataset"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "the kinetics human action video dataset"
    - "pku mmd a large scale benchmark for continuous multi modal human action understanding"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "action recognition based on joint trajectory maps using convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "cricket stroke extraction towards creation of a large scale cricket actions dataset"):
  with members:
    - "the kinetics human action video dataset"
    - "turn tap temporal unit regression network for temporal action proposals"
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "temporal segment networks towards good practices for deep action recognition"
    - "youtube 8m a large scale video classification benchmark"
    - "tubelets unsupervised action proposals from spatiotemporal super voxels"
    - "end to end learning of action detection from frame glimpses in videos"
    - "a deep siamese network for scene detection in broadcast videos"

Connected through hyperedge HE-6 (induced by source paper "let s dance learning from online dance videos"):
  with members:
    - "the kinetics human action video dataset"
    - "ava a video dataset of spatio temporally localized atomic visual actions"
    - "convolutional pose machines"
    - "spatio temporal lstm with trust gates for 3d human action recognition"
    - "convolutional two stream network fusion for video action recognition"
    - "you only look once unified real time object detection"
    - "unsupervised learning of video representations using lstms"
    - "p cnn pose based cnn features for action recognition"

Connected through hyperedge HE-7 (induced by source paper "two stream rnn cnn for action recognition in 3d videos"):
  with members:
    - "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"
    - "a deep structured model with radius margin bound for 3d human activity recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "histogram of oriented principal components for cross view action recognition"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "cross view action modeling learning and recognition"

Connected through hyperedge HE-8 (induced by source paper "pku mmd a large scale benchmark for continuous multi modal human action understanding"):
  with members:
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "online action detection"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"
    - "an end to end spatio temporal attention model for human action recognition from skeleton data"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "temporal segment networks towards good practices for deep action recognition"
    - "rgb d based action recognition datasets a survey"
    - "modeling spatial temporal clues in a hybrid deep learning framework for video classification"

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)