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: "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.

Connected through hyperedge HE-1 (induced by source paper "exploiting human social cognition for the detection of fake and fraudulent faces via memory networks"):
  with members:
    - "multi task learning for detecting and segmenting manipulated facial images and videos"
    - "faceforensics learning to detect manipulated facial images"
    - "recurrent convolutional strategies for face manipulation detection in videos"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "mesonet a compact facial video forgery detection network"
    - "forensictransfer weakly supervised domain adaptation for forgery detection"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "pedestrian trajectory prediction with structured memory hierarchies"

Connected through hyperedge HE-2 (induced by source paper "coupled generative adversarial network for continuous fine grained action segmentation"):
  with members:
    - "stabilizing gan training with multiple random projections"
    - "stabilizing training of generative adversarial networks through regularization"
    - "tricornet a hybrid temporal convolutional and recurrent network for video action segmentation"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "semi supervised learning with context conditional generative adversarial networks"
    - "two stream lstm a deep fusion framework for human action recognition"
    - "seqgan sequence generative adversarial nets with policy gradient"

Connected through hyperedge HE-3 (induced by source paper "cmsalgan rgb d salient object detection with cross view generative adversarial networks"):
  with members:
    - "adaptive fusion for rgb d salient object detection"
    - "coupled generative adversarial network for continuous fine grained action segmentation"
    - "a simple pooling based design for real time salient object detection"
    - "sch gan semi supervised cross modal hashing by generative adversarial network"
    - "unsupervised cross modality domain adaptation of convnets for biomedical image segmentations with adversarial loss"
    - "self supervised adversarial hashing networks for cross modal retrieval"
    - "macro micro adversarial network for human parsing"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-4 (induced by source paper "memory augmented deep generative models for forecasting the next shot location in tennis"):
  with members:
    - "deep decision trees for discriminative dictionary learning with adversarial multi agent trajectories"
    - "learning temporal strategic relationships using generative adversarial imitation learning"
    - "neural map structured memory for deep reinforcement learning"
    - "tree memory networks for modelling long term temporal dependencies"
    - "superposed episodic and semantic memory via sparse distributed representation"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "semi supervised learning with context conditional generative adversarial networks"
    - "one shot imitation learning"

Connected through hyperedge HE-5 (induced by source paper "neural memory plasticity for anomaly detection"):
  with members:
    - "an overview of deep learning based methods for unsupervised and semi supervised anomaly detection in videos"
    - "brain tumor type classification via capsule networks"
    - "pedestrian trajectory prediction with structured memory hierarchies"
    - "learning temporal strategic relationships using generative adversarial imitation learning"
    - "learning dynamic memory networks for object tracking"
    - "applications of deep learning and reinforcement learning to biological data"
    - "tree memory networks for modelling long term temporal dependencies"
    - "abnormal event detection in videos using spatiotemporal autoencoder"

Connected through hyperedge HE-6 (induced by source paper "learning temporal strategic relationships using generative adversarial imitation learning"):
  with members:
    - "tracking by prediction a deep generative model for mutli person localisation and tracking"
    - "simultaneous policy learning and latent state inference for imitating driver behavior"
    - "imitating driver behavior with generative adversarial networks"
    - "neural map structured memory for deep reinforcement learning"
    - "tree memory networks for modelling long term temporal dependencies"
    - "soft hardwired attention an lstm framework for human trajectory prediction and abnormal event detection"
    - "third person imitation learning"
    - "infogail interpretable imitation learning from visual demonstrations"

Connected through hyperedge HE-7 (induced by source paper "multi level sequence gan for group activity recognition"):
  with members:
    - "tracking by prediction a deep generative model for mutli person localisation and tracking"
    - "discrimnet semi supervised action recognition from videos using generative adversarial networks"
    - "structural recurrent neural network srnn for group activity analysis"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "semi supervised learning with context conditional generative adversarial networks"
    - "two stream lstm a deep fusion framework for human action recognition"
    - "cern confidence energy recurrent network for group activity recognition"
    - "pixel level domain transfer"

Connected through hyperedge HE-8 (induced by source paper "tracking by prediction a deep generative model for mutli person localisation and tracking"):
  with members:
    - "tree memory networks for modelling long term temporal dependencies"
    - "soft hardwired attention an lstm framework for human trajectory prediction and abnormal event detection"
    - "two stream lstm a deep fusion framework for human action recognition"
    - "deep multi camera people detection"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "deep occlusion reasoning for multi camera multi target detection"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"

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)