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: "real time human motion capture with multiple depth cameras"
  Abstract: Commonly used human motion capture systems require intrusive attachment of markers that are visually tracked with multiple cameras. In this work we present an efficient and inexpensive solution to markerless motion capture using only a few Kinect sensors. Unlike the previous work on 3d pose estimation using a single depth camera, we relax constraints on the camera location and do not assume a co-operative user. We apply recent image segmentation techniques to depth images and use curriculum learning to train our system on purely synthetic data. Our method accurately localizes body parts without requiring an explicit shape model. The body joint locations are then recovered by combining evidence from multiple views in real-time. We also introduce a dataset of ~6 million synthetic depth frames for pose estimation from multiple cameras and exceed state-of-the-art results on the Berkeley MHAD dataset.

Connected through hyperedge HE-1 (induced by source paper "human action recognition using deep multilevel multimodal m2 fusion of depth and inertial sensors"):
  with members:
    - "lstm a search space odyssey"
    - "a tutorial on canonical correlation methods"
    - "classification of time series images using deep convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "joint attention in driver pedestrian interaction from theory to practice"):
  with members:
    - "when vehicles see pedestrians with phones a multi cue framework for recognizing phone based activities of pedestrians"
    - "video salient object detection via fully convolutional networks"
    - "agreeing to cross how drivers and pedestrians communicate"
    - "citypersons a diverse dataset for pedestrian detection"
    - "integrating three mechanisms of visual attention for active visual search"
    - "learning where to attend like a human driver"
    - "hcp a flexible cnn framework for multi label image classification"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-3 (induced by source paper "3d human pose estimation from depth maps using a deep combination of poses"):
  with members:
    - "towards viewpoint invariant 3d human pose estimation"
    - "predicting people s 3d poses from short sequences"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "matconvnet convolutional neural networks for matlab"

Connected through hyperedge HE-4 (induced by source paper "play and learn using video games to train computer vision models"):
  with members:
    - "the cityscapes dataset for semantic urban scene understanding"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "playing for data ground truth from computer games"
    - "synthetic data for text localisation in natural images"
    - "semantic image segmentation via deep parsing network"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"
    - "conditional random fields as recurrent neural networks"
    - "deep residual learning for image recognition"

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)