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 "kldivnet an unsupervised neural network for multi modality image registration"):
  Abstract: Multi-modality image registration is one of the most underlined processes in medical image analysis. Recently, convolutional neural networks (CNNs) have shown significant potential in deformable registration. However, the lack of voxel-wise ground truth challenges the training of CNNs for an accurate registration. In this work, we propose a cross-modality similarity metric, based on the KL-divergence of image variables, and implement an efficient estimation method using a CNN. This estimation network, referred to as KLDivNet, can be trained unsupervisedly. We then embed the KLDivNet into a registration network to achieve the unsupervised deformable registration for multi-modality images. We employed three datasets, i.e., AAL Brain, LiTS Liver and Hospital Liver, with both the intra- and inter-modality image registration tasks for validation. Results showed that our similarity metric was effective, and the proposed registration network delivered superior performance compared to the state-of-the-art methods.

Includes members:
  Member "the liver tumor segmentation benchmark lits":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi organ segmentation over partially labeled datasets with multi scale feature abstraction")
  Member "airlab autograd image registration laboratory":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "drmime differentiable mutual information and matrix exponential for multi resolution image registration")
  Member "formal limitations on the measurement of mutual information":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on predictive information in rnns")
  Member "an unsupervised learning model for deformable medical image registration":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns")

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)