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: "m2cai workflow challenge convolutional neural networks with time smoothing and hidden markov model for video frames classification"
  Abstract: Our approach is among the three best to tackle the M2CAI Workflow challenge. The latter consists in recognizing the operation phase for each frames of endoscopic videos. In this technical report, we compare several classification models and temporal smoothing methods. Our submitted solution is a fine tuned Residual Network-200 on 80% of the training set with temporal smoothing using simple temporal averaging of the predictions and a Hidden Markov Model modeling the sequence.

Connected through hyperedge HE-1 (induced by source paper "multi task recurrent convolutional network with correlation loss for surgical video analysis"):
  with members:
    - "incorporating temporal prior from motion flow for instrument segmentation in minimally invasive surgery video"
    - "deep reinforcement learning for surgical gesture segmentation and classification"
    - "uncertainty in multitask learning joint representations for probabilistic mr only radiotherapy planning"
    - "less is more surgical phase recognition with less annotations through self supervised pre training of cnn lstm networks"
    - "deepphase surgical phase recognition in cataracts videos"
    - "multi task learning of pairwise sequence classification tasks over disparate label spaces"
    - "automated pulmonary nodule detection via 3d convnets with online sample filtering and hybrid loss residual learning"
    - "fine grained recognition in the wild a multi task domain adaptation approach"

Connected through hyperedge HE-2 (induced by source paper "less is more surgical phase recognition with less annotations through self supervised pre training of cnn lstm networks"):
  with members:
    - "multi task self supervised visual learning"
    - "unsupervised representation learning by sorting sequences"
    - "monitoring tool usage in cataract surgery videos using boosted convolutional and recurrent neural networks"
    - "learning representations for automatic colorization"
    - "endonet a deep architecture for recognition tasks on laparoscopic videos"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "colorful image colorization"
    - "real time analysis of cataract surgery videos using statistical models"

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:
    - "tool and phase recognition using contextual cnn features"
    - "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"
    - "tool and phase recognition using contextual cnn features"
    - "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"

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)