human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "fully convolutional densenets for polyp segmentation in colonoscopy"):
  Abstract: Early diagnosis and resection of colorectal polyps can effectively reduce the incidence and mortality rate. Colorectal cancer is a common gastrointestinal malignancy, ranking one of the three major malignancies around the world. With the improvement of living standards and dietary habits related problems, the incidence and mortality of colorectal cancer are showing an upward trend. Colorectal cancer is mostly from adenoma polyp malignant change, so early detection has important clinical significance. Although colonoscopy conducted by doctors is considered the most effective way in detecting polyps, uncertainty such as fatigue can affect the results. To solve this problem, we propose a fully convolutional densenet method to achieve the automatic detection and segmentation of colorectal polyps by computer. In this paper, we apply densenet to full convolutional network in segmentation of colorectal polyp, under the condition that not requiring post-processing and pre-training situation, we compare the number of parameters in different layers and assess accuracy and IOU respectively in segmentation of colorectal polyps. The results show that accuracy is improved as the layer increases gradually. When the layer number is 78(N=78), accuracy reaches 97.1% and the average IOU is 83.4%. In addition, we make a comparison with the state-of-the-art polyp segmentation method, the results reveal our method make a considerable improvement.

Includes members:
  Member "fully convolutional adaptation networks for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "label driven reconstruction for domain adaptation in semantic segmentation")
  Member "bpds a blockchain based privacy preserving data sharing for electronic medical records":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scaling blockchains to support electronic health records for hospital systems")
  Member "secure and trustable electronic medical records sharing using blockchain":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "attribute based multi signature and encryption for ehr management a blockchain based solution")
  Member "a benchmark for endoluminal scene segmentation of colonoscopy images":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a learning without forgetting approach to incorporate artifact knowledge in polyp localization tasks")
  Member "u net convolutional networks for biomedical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "faster r cnn towards real time object detection with region proposal networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "delving deep into rectifiers surpassing human level performance on imagenet classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

Question: Please tell me which class the source paper 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.SE(Software Engineering)