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: "a deep metric for multimodal registration"
  Abstract: Multimodal registration is a challenging problem in medical imaging due the high variability of tissue appearance under different imaging modalities. The crucial component here is the choice of the right similarity measure. We make a step towards a general learning-based solution that can be adapted to specific situations and present a metric based on a convolutional neural network. Our network can be trained from scratch even from a few aligned image pairs. The metric is validated on intersubject deformable registration on a dataset different from the one used for training, demonstrating good generalization. In this task, we outperform mutual information by a significant margin.

Connected through hyperedge HE-1 (induced by source paper "finite sample analysis of image registration"):
  with members:
    - "estimators for multivariate information measures in general probability spaces"
    - "end to end unsupervised deformable image registration with a convolutional neural network"
    - "fundamental limits in multi image alignment"
    - "demystifying fixed k nearest neighbor information estimators"
    - "asymptotic estimates in information theory with non vanishing error probabilities"
    - "asymptotics and non asymptotics for universal fixed to variable source coding"
    - "channels with cost constraints strong converse and dispersion"
    - "a tight upper bound for the third order asymptotics for most discrete memoryless channels"

Connected through hyperedge HE-2 (induced by source paper "on single source robustness in deep fusion models"):
  with members:
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "robust deep multi modal learning based on gated information fusion network"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "state of the art speech recognition with sequence to sequence models"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "mask r cnn"
    - "choosing smartly adaptive multimodal fusion for object detection in changing environments"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-3 (induced by source paper "automatic registration between cone beam ct and scanned surface via deep pose regression neural networks and clustered similarities"):
  with members:
    - "weakly supervised convolutional neural networks for multimodal image registration"
    - "a survey on deep learning in medical image analysis"
    - "quicksilver fast predictive image registration a deep learning approach"
    - "go icp a globally optimal solution to 3d icp point set registration"
    - "fast predictive image registration"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "weakly supervised learning of metric aggregations for deformable image registration"):
  with members:
    - "label driven weakly supervised learning for multimodal deformarle image registration"
    - "slice to volume medical image registration a survey"
    - "ssemnet serial section electron microscopy image registration using a spatial transformer network with learned features"
    - "deformable registration through learning of context specific metric aggregation"
    - "spatial transformer networks"
    - "learning to compare image patches via convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "communal domain learning for registration in drifted image spaces"):
  with members:
    - "a deep learning framework for unsupervised affine and deformable image registration"
    - "deep learning based inter modality image registration supervised by intra modality similarity"
    - "a kernel method for the two sample problem"

Connected through hyperedge HE-6 (induced by source paper "deep information theoretic registration"):
  with members:
    - "an unsupervised learning model for deformable medical image registration"
    - "learning to compare image patches via convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "learning deep similarity metric for 3d mr trus image registration"):
  with members:
    - "learning to compare image patches via convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "quantitative phase imaging and artificial intelligence a review"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "virtual histological staining of unlabelled tissue autofluorescence images via deep learning"
    - "extended depth of field in holographic imaging using deep learning based autofocusing and phase recovery"
    - "phase recovery and holographic image reconstruction using deep learning in neural networks"
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "choicenet robust learning by revealing output correlations"
    - "deep learning enhanced mobile phone microscopy"
    - "attention u net learning where to look for the pancreas"

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)