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 deep structured model with radius margin bound for 3d human activity recognition"
  Abstract: Understanding human activity is very challenging even with the recently developed 3D/depth sensors. To solve this problem, this work investigates a novel deep structured model, which adaptively decomposes an activity instance into temporal parts using the convolutional neural networks. Our model advances the traditional deep learning approaches in two aspects. First, we incorporate latent temporal structure into the deep model, accounting for large temporal variations of diverse human activities. In particular, we utilize the latent variables to decompose the input activity into a number of temporally segmented sub-activities, and accordingly feed them into the parts (i.e. sub-networks) of the deep architecture. Second, we incorporate a radius---margin bound as a regularization term into our deep model, which effectively improves the generalization performance for classification. For model training, we propose a principled learning algorithm that iteratively (i) discovers the optimal latent variables (i.e. the ways of activity decomposition) for all training instances, (ii) updates the classifiers based on the generated features, and (iii) updates the parameters of multi-layer neural networks. In the experiments, our approach is validated on several complex scenarios for human activity recognition and demonstrates superior performances over other state-of-the-art approaches.

Connected through hyperedge HE-1 (induced by source paper "learning deep representations for semantic image parsing a comprehensive overview"):
  with members:
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "deep supervision for pancreatic cyst segmentation in abdominal ct scans"
    - "progressively diffused networks for semantic image segmentation"
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"
    - "knowledge guided recurrent neural network learning for task oriented action prediction"
    - "interpretable structure evolving lstm"
    - "label refinement network for coarse to fine semantic segmentation"
    - "instance level salient object segmentation"

Connected through hyperedge HE-2 (induced by source paper "neural task planning with and or graph representations"):
  with members:
    - "fine grained representation learning and recognition by exploiting hierarchical semantic embedding"
    - "learning model based planning from scratch"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "recurrent attentional reinforcement learning for multi label image recognition"
    - "knowledge guided recurrent neural network learning for task oriented action prediction"
    - "diversified visual attention networks for fine grained object classification"
    - "visual semantic planning using deep successor representations"
    - "cognitive mapping and planning for visual navigation"

Connected through hyperedge HE-3 (induced by source paper "learning deep similarity models with focus ranking for fabric image retrieval"):
  with members:
    - "multi label image recognition by recurrently discovering attentional regions"
    - "learning a wavelet like auto encoder to accelerate deep neural networks"
    - "disc deep image saliency computing via progressive representation learning"
    - "deep ranking for person re identification via joint representation learning"
    - "facenet a unified embedding for face recognition and clustering"
    - "fast r cnn"
    - "deep metric learning via lifted structured feature embedding"
    - "bit scalable deep hashing with regularized similarity learning for image retrieval and person re identification"

Connected through hyperedge HE-4 (induced by source paper "pose conditioned spatio temporal attention for human action recognition"):
  with members:
    - "structured attention networks"
    - "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"
    - "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"
    - "deep multimodal feature analysis for action recognition in rgb d videos"
    - "youtube 8m a large scale video classification benchmark"

Connected through hyperedge HE-5 (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"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "3d human activity recognition with reconfigurable convolutional neural networks"
    - "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-6 (induced by source paper "content adaptive sketch portrait generation by decompositional representation learning"):
  with members:
    - "cross domain visual matching via generalized similarity measure and feature learning"
    - "disc deep image saliency computing via progressive representation learning"
    - "fast r cnn"
    - "bit scalable deep hashing with regularized similarity learning for image retrieval and person re identification"
    - "discriminatively trained and or graph models for object shape detection"
    - "image super resolution using deep convolutional networks"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "designing deep networks for surface normal estimation"

Connected through hyperedge HE-7 (induced by source paper "human pose estimation from depth images via inference embedded multi task learning"):
  with members:
    - "cost effective active learning for deep image classification"
    - "cross domain visual matching via generalized similarity measure and feature learning"
    - "dari distance metric and representation integration for person verification"
    - "deep structured scene parsing by learning with image descriptions"
    - "geometric scene parsing with hierarchical lstm"
    - "structured feature learning for pose estimation"
    - "semantic object parsing with local global long short term memory"
    - "deep human parsing with active template regression"

Connected through hyperedge HE-8 (induced by source paper "spatio temporal lstm with trust gates for 3d human action recognition"):
  with members:
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "gated siamese convolutional neural network architecture for human re identification"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"
    - "online human action detection using joint classification regression recurrent neural networks"
    - "deep multimodal feature analysis for action recognition in rgb d videos"
    - "a siamese long short term memory architecture for human re identification"
    - "space time representation of people based on 3d skeletal data a review"
    - "a hierarchical pose based approach to complex action understanding using dictionaries of actionlets and motion poselets"

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)