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: "the tum lapchole dataset for the m2cai 2016 workflow challenge"
  Abstract: In this technical report we present our collected dataset of laparoscopic cholecystectomies (LapChole). Laparoscopic videos of a total of 20 surgeries were recorded and annotated with surgical phase labels, of which 15 were randomly pre-determined as training data, while the remaining 5 videos are selected as test data. This dataset was later included as part of the M2CAI 2016 workflow detection challenge during MICCAI 2016 in Athens.

Connected through hyperedge HE-1 (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"
    - "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"
    - "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-2 (induced by source paper "deepphase surgical phase recognition in cataracts videos"):
  with members:
    - "surgical data science for next generation interventions"
    - "endonet a deep architecture for recognition tasks on laparoscopic videos"
    - "temporal convolutional networks a unified approach to action segmentation"
    - "deep residual learning for image recognition"
    - "imagenet large scale visual recognition challenge"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "progress estimation and phase detection for sequential processes"):
  with members:
    - "endonet a deep architecture for recognition tasks on laparoscopic videos"
    - "an end to end generative framework for video segmentation and recognition"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "visualizing and understanding recurrent networks"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "visualizing and understanding convolutional networks"

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)