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: "single and multi task architectures for tool presence detection challenge at m2cai 2016"
  Abstract: The tool presence detection challenge at M2CAI 2016 consists of identifying the presence/absence of seven surgical tools in the images of cholecystectomy videos. Here, we propose to use deep architectures that are based on our previous work where we presented several architectures to perform multiple recognition tasks on laparoscopic videos. In this technical report, we present the tool presence detection results using two architectures: (1) a single-task architecture designed to perform solely the tool presence detection task and (2) a multi-task architecture designed to perform jointly phase recognition and tool presence detection. The results show that the multi-task network only slightly improves the tool presence detection results. In constrast, a significant improvement is obtained when there are more data available to train the networks. This significant improvement can be regarded as a call for action for other institutions to start working toward publishing more datasets into the community, so that better models could be generated to perform the task.

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"
    - "tool and phase recognition using contextual cnn features"
    - "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 "learning from a tiny dataset of manual annotations a teacher student approach for surgical phase recognition"):
  with members:
    - "rsdnet learning to predict remaining surgery duration from laparoscopic videos without manual annotations"
    - "temporal coherence based self supervised learning for laparoscopic workflow analysis"
    - "less is more surgical phase recognition with less annotations through self supervised pre training of cnn lstm networks"
    - "unsupervised temporal context learning using convolutional neural networks for laparoscopic workflow analysis"
    - "data distillation towards omni supervised learning"
    - "identity mappings in deep residual networks"
    - "endonet a deep architecture for recognition tasks on laparoscopic videos"
    - "neural architectures for named entity recognition"

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

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)