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: "two stream convolutional neural network for accurate rgb d fingertip detection using depth and edge information"
  Abstract: Accurate detection of fingertips in depth image is critical for human-computer interaction. In this paper, we present a novel two-stream convolutional neural network (CNN) for RGB-D fingertip detection. Firstly edge image is extracted from raw depth image using random forest. Then the edge information is combined with depth information in our CNN structure. We study several fusion approaches and suggest a slow fusion strategy as a promising way of fingertip detection. As shown in our experiments, our real-time algorithm outperforms state-of-the-art fingertip detection methods on the public dataset HandNet with an average 3D error of 9.9mm, and shows comparable accuracy of fingertip estimation on NYU hand dataset.

Connected through hyperedge HE-1 (induced by source paper "efficiently utilizing complex valued polsar image data via a multi task deep learning framework"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "polarimetric convolutional network for polsar image classification"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "hierarchical representations for efficient architecture search"
    - "sar image despeckling using a convolutional neural network"

Connected through hyperedge HE-2 (induced by source paper "deephps end to end estimation of 3d hand pose and shape by learning from synthetic depth"):
  with members:
    - "pose guided structured region ensemble network for cascaded hand pose estimation"
    - "crossing nets combining gans and vaes with a shared latent space for hand pose estimation"
    - "occlusion aware hand pose estimation using hierarchical mixture density network"
    - "learning to estimate 3d hand pose from single rgb images"
    - "using a single rgb frame for real time 3d hand pose estimation in the wild"
    - "v2v posenet voxel to voxel prediction network for accurate 3d hand and human pose estimation from a single depth map"
    - "hand3d hand pose estimation using 3d neural network"
    - "simultaneous hand pose and skeleton bone lengths estimation from a single depth image"

Connected through hyperedge HE-3 (induced by source paper "deep learning for electromyographic hand gesture signal classification by leveraging transfer learning"):
  with members:
    - "progressive neural networks"
    - "adaptive learning to speed up control of prosthetic hands a few things everybody should know"
    - "revisiting batch normalization for practical domain adaptation"
    - "eie efficient inference engine on compressed deep neural network"
    - "theano a python framework for fast computation of mathematical expressions"
    - "parametric exponential linear unit for deep convolutional neural networks"
    - "quantized convolutional neural networks for mobile devices"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "beyond rgb very high resolution urban remote sensing with multimodal deep networks"):
  with members:
    - "joint learning from earth observation and openstreetmap data to get faster better semantic maps"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "adversarial examples for semantic segmentation and object detection"
    - "towards better exploiting convolutional neural networks for remote sensing scene classification"
    - "full resolution residual networks for semantic segmentation in street scenes"
    - "fully convolutional networks for dense semantic labelling of high resolution aerial imagery"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "how useful is region based classification of remote sensing images in a deep learning framework"

Connected through hyperedge HE-5 (induced by source paper "simultaneous hand pose and skeleton bone lengths estimation from a single depth image"):
  with members:
    - "learning to estimate 3d hand pose from single rgb images"
    - "hand3d hand pose estimation using 3d neural network"
    - "region ensemble network improving convolutional network for hand pose estimation"
    - "end to end global to local cnn learning for hand pose recovery in depth data"
    - "training a feedback loop for hand pose estimation"
    - "robust 3d hand pose estimation in single depth images from single view cnn to multi view cnns"
    - "spatial attention deep net with partial pso for hierarchical hybrid hand pose estimation"
    - "learning to search on manifolds for 3d pose estimation of articulated objects"

Connected through hyperedge HE-6 (induced by source paper "region ensemble network improving convolutional network for hand pose estimation"):
  with members:
    - "training a feedback loop for hand pose estimation"
    - "robust 3d hand pose estimation in single depth images from single view cnn to multi view cnns"
    - "learning to search on manifolds for 3d pose estimation of articulated objects"
    - "network of experts for large scale image categorization"
    - "human pose estimation via convolutional part heatmap regression"
    - "model based deep hand pose estimation"
    - "deeptrack learning discriminative feature representations online for robust visual tracking"
    - "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)