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: "crowdsourcing for identification of polyp free segments in virtual colonoscopy videos"
  Abstract: Virtual colonoscopy (VC) allows a physician to virtually navigate within a reconstructed 3D colon model searching#N#for colorectal polyps. Though VC is widely recognized as a highly sensitive and specific test for identifying#N#polyps, one limitation is the reading time, which can take over 30 minutes per patient. Large amounts of the#N#colon are often devoid of polyps, and a way of identifying these polyp-free segments could be of valuable use in#N#reducing the required reading time for the interrogating radiologist. To this end, we have tested the ability of#N#the collective crowd intelligence of non-expert workers to identify polyp candidates and polyp-free regions. We#N#presented twenty short videos flying through a segment of a virtual colon to each worker, and the crowd was#N#asked to determine whether or not a possible polyp was observed within that video segment. We evaluated our#N#framework on Amazon Mechanical Turk and found that the crowd was able to achieve a sensitivity of 80.0% and#N#specificity of 86.5% in identifying video segments which contained a clinically proven polyp. Since each polyp#N#appeared in multiple consecutive segments, all polyps were in fact identified. Using the crowd results as a first#N#pass, 80% of the video segments could in theory be skipped by the radiologist, equating to a significant time#N#savings and enabling more VC examinations to be performed.

Connected through hyperedge HE-1 (induced by source paper "a survey of crowdsourcing in medical image analysis"):
  with members:
    - "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"
    - "crowd disagreement of medical images is informative"
    - "crowd assisted polyp annotation of virtual colonoscopy videos"
    - "employing weak annotations for medical image analysis problems"
    - "expected exponential loss for gaze based video and volume ground truth annotation"
    - "a data driven analysis of workers earnings on amazon mechanical turk"
    - "negative results in computer vision a perspective"
    - "a survey on deep learning in medical image analysis"

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.HC(Human-Computer Interaction)