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 "a kings ransom for encryption ransomware classification using augmented one shot learning and bayesian approximation"):
  Abstract: Newly emerging variants of ransomware pose an ever-growing threat to computer systems governing every aspect of modern life through the handling and analysis of big data. While various recent security-based approaches have focused on detecting and classifying ransomware at the network or system level, easy-to-use post-infection ransomware classification for the lay user has not been attempted before. In this paper, we investigate the possibility of classifying the ransomware a system is infected with simply based on a screenshot of the splash screen or the ransom note captured using a consumer camera commonly found in any modern mobile device. To train and evaluate our system, we create a sample dataset of the splash screens of 50 well-known ransomware variants. In our dataset, only a single training image is available per ransomware. Instead of creating a large training dataset of ransomware screenshots, we simulate screenshot capture conditions via carefully designed data augmentation techniques, enabling simple and efficient one-shot learning. Moreover, using model uncertainty obtained via Bayesian approximation, we ensure special input cases such as unrelated non-ransomware images and previously-unseen ransomware variants are correctly identified for special handling and not mis-classified. Extensive experimental evaluation demonstrates the efficacy of our work, with accuracy levels of up to 93.6% for ransomware classification.

Includes members:
  Member "data augmentation using learned transformations for one shot medical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "lt net label transfer by learning reversible voxel wise correspondence for one shot medical image segmentation")
  Member "veritatem dies aperit temporally consistent depth prediction enabled by a multi task geometric and semantic scene understanding approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "to complete or to estimate that is the question a multi task approach to depth completion and monocular depth estimation")
  Member "temporal graph offset reconstruction towards temporally robust graph representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "temporal neighbourhood aggregation predicting future links in temporal graphs via recurrent variational graph convolutions")
  Member "mobilenetv2 inverted residuals and linear bottlenecks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "bayesian layers a module for neural network uncertainty":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "planet of the bayesians reconsidering and improving deep planning network by incorporating bayesian inference")
  Member "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lossless compression of deep neural networks")
  Member "what uncertainties do we need in bayesian deep learning for computer vision":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "one shot learning with memory augmented neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "incremental few shot object detection")

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)