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 "task specific visual saliency prediction with memory augmented conditional generative adversarial networks"):
  Abstract: Visual saliency patterns are the result of a variety of factors aside from the image being parsed, however existing approaches have ignored these. To address this limitation, we propose a novel saliency estimation model which leverages the semantic modelling power of conditional generative adversarial networks together with memory architectures which capture the subject's behavioural patterns and task dependent factors. We make contributions aiming to bridge the gap between bottom-up feature learning capabilities in modern deep learning architectures and traditional top-down hand-crafted features based methods for task specific saliency modelling. The conditional nature of the proposed framework enables us to learn contextual semantics and relationships among different tasks together, instead of learning them separately for each task. Our studies not only shed light on a novel application area for generative adversarial networks, but also emphasise the importance of task specific saliency modelling and demonstrate the plausibility of fully capturing this context via an augmented memory architecture.

Includes members:
  Member "end to end saliency mapping via probability distribution prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "stavis spatio temporal audiovisual saliency network")
  Member "tree memory networks for modelling long term temporal dependencies":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "exploiting human social cognition for the detection of fake and fraudulent faces via memory networks")
  Member "soft hardwired attention an lstm framework for human trajectory prediction and abnormal event detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "social wagdat interaction aware trajectory prediction via wasserstein graph double attention network")
  Member "salgan visual saliency prediction with generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "two stream lstm a deep fusion framework for human action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "automatic video object segmentation via motion appearance stream fusion and instance aware segmentation")
  Member "a deep multi level network for saliency prediction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "tidying deep saliency prediction architectures")
  Member "precomputed real time texture synthesis with markovian generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "collaborative distillation for ultra resolution universal style transfer")
  Member "pixel level domain transfer":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "show attend and translate unsupervised image translation with self regularization and attention")

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)