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: "tool and phase recognition using contextual cnn features"
  Abstract: A transfer learning method for generating features suitable for surgical tools and phase recognition from the ImageNet classification features [1] is proposed here. In addition, methods are developed for generating contextual features and combining them with time series analysis for final classification using multi-class random forest. The proposed pipeline is tested over the training and testing datasets of M2CAI16 challenges: tool and phase detection. Encouraging results are obtained by leave-one-out cross validation evaluation on the training dataset.

Connected through hyperedge HE-1 (induced by source paper "laptool net a contextual detector of surgical tools in laparoscopic videos based on recurrent convolutional neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"
    - "multi label classification of surgical tools with convolutional neural networks"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "a survey on deep learning in medical image analysis"
    - "single and multi task architectures for tool presence detection challenge at m2cai 2016"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "endonet a deep architecture for recognition tasks on laparoscopic videos"

Connected through hyperedge HE-2 (induced by source paper "a deep learning approach for pose estimation from volumetric oct data"):
  with members:
    - "brain tumor segmentation with deep neural networks"
    - "a survey on deep learning in medical image analysis"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "aggregated residual transformations for deep neural networks"
    - "deep learning of local rgb d patches for 3d object detection and 6d pose estimation"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "densely connected convolutional networks"
    - "3d u net learning dense volumetric segmentation from sparse annotation"

Connected through hyperedge HE-3 (induced by source paper "tool detection and operative skill assessment in surgical videos using region based convolutional neural networks"):
  with members:
    - "m2cai workflow challenge convolutional neural networks with time smoothing and hidden markov model for video frames classification"
    - "single and multi task architectures for tool presence detection challenge at m2cai 2016"
    - "endonet a deep architecture for recognition tasks on laparoscopic videos"
    - "the tum lapchole dataset for the m2cai 2016 workflow challenge"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-4 (induced by source paper "monitoring tool usage in cataract surgery videos using boosted convolutional and recurrent neural networks"):
  with members:
    - "deep image mining for diabetic retinopathy screening"
    - "learning transferable architectures for scalable image recognition"
    - "unsupervised temporal context learning using convolutional neural networks for laparoscopic workflow analysis"
    - "two stream lstm a deep fusion framework for human action recognition"
    - "m2cai workflow challenge convolutional neural networks with time smoothing and hidden markov model for video frames classification"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"
    - "single and multi task architectures for surgical workflow challenge at m2cai 2016"

Connected through hyperedge HE-5 (induced by source paper "m2cai workflow challenge convolutional neural networks with time smoothing and hidden markov model for video frames classification"):
  with members:
    - "identity mappings in deep residual networks"
    - "all you need is a good init"
    - "rethinking the inception architecture for computer vision"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale 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)