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 "pilot study on verifying the monotonic relationship between error and uncertainty in deformable registration for neurosurgery"):
  Abstract: In image-guided neurosurgery, deformable registration currently is not a clinical routine. Although using it in practice is a goal for image-guided therapy, this goal is hampered because surgeons are wary of the less predictable deformable registration error. In the preoperative- to-intraoperative registration, when surgeons notice a misaligned image pattern, they want to know whether it is a registration error or an actual deformation caused by tumor resection or retraction. Here, surgeons need a spatial distribution of error to help them make a better-informed decision, i.e., ignore locations with high error. However, such an error estimate is difficult to acquire. Alternatively, probabilistic image registration (PIR) methods give measures of registration uncertainty, which is a potential surrogate for assessing the quality of registration results. It is intuitive and believed by a lot of people that high uncertainty indicates a large error. Yet to the best of our knowledge, no such conclusion has been reported in the PIR literature. In this study, we look at one PIR method and give preliminary results showing that point-wise registration error and uncertainty are monotonically correlated.

Includes members:
  Member "unsupervised learning for fast probabilistic diffeomorphic registration":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unsupervised multi modal image registration via geometry preserving image to image translation")
  Member "a feature driven active framework for ultrasound based brain shift compensation":
    also connected through other hyperedges:
      (none)
  Member "probabilistic diffeomorphic registration representing uncertainty":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning a probabilistic model for diffeomorphic registration")

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.CV(Computer Vision and Pattern Recognition)