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 "deep learning cardiac motion analysis for human survival prediction"):
  Abstract: Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimizing the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4Dsurvival), which is a hybrid network consisting of an autoencoder that learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimized for survival prediction. To handle right-censored survival outcomes, our network used a Cox partial likelihood loss function. In a study of 302 patients, the predictive accuracy (quantified by Harrell’s C-index) was significantly higher (P = 0.0012) for our model C = 0.75 (95% CI: 0.70–0.79) than the human benchmark of C = 0.59 (95% CI: 0.53–0.65). This work demonstrates how a complex computer vision task using high-dimensional medical image data can efficiently predict human survival. A fully convolutional neural network is used to create time-resolved three-dimensional dense segmentations of heart images. This dense motion model forms the input to a supervised system called 4Dsurvival that can efficiently predict human survival.

Includes members:
  Member "automatic 3d bi ventricular segmentation of cardiac images by a shape constrained multi task deep learning approach":
    also connected through other hyperedges:
      (none)
  Member "learning interpretable anatomical features through deep generative models application to cardiac remodeling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on explainable artificial intelligence xai towards medical xai")
  Member "deep representation learning for human motion prediction and classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction")
  Member "automated cardiovascular magnetic resonance image analysis with fully convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "model based and data driven strategies in medical image computing")
  Member "a survey on deep learning in medical image analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "medgan medical image translation using gans")
  Member "an uncertain future forecasting from static images using variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "discriminative recurrent sparse auto encoders":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "randnet deep learning with compressed measurements of images")

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.LG(Machine Learning)