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: "pathgan visual scanpath prediction with generative adversarial networks"
  Abstract: We introduce PathGAN, a deep neural network for visual scanpath prediction trained on adversarial examples. A visual scanpath is defined as the sequence of fixation points over an image defined by a human observer with its gaze. PathGAN is composed of two parts, the generator and the discriminator. Both parts extract features from images using off-the-shelf networks, and train recurrent layers to generate or discriminate scanpaths accordingly. In scanpath prediction, the stochastic nature of the data makes it very difficult to generate realistic predictions using supervised learning strategies, but we adopt adversarial training as a suitable alternative. Our experiments prove how PathGAN improves the state of the art of visual scanpath prediction on the iSUN and Salient360! datasets. Source code and models are available at this https URL

Connected through hyperedge HE-1 (induced by source paper "fully convolutional neural networks for raw eye tracking data segmentation generation and reconstruction"):
  with members:
    - "eye movement velocity and gaze data generator for evaluation robustness testing and assess of eye tracking software and visualization tools"
    - "end to end eye movement detection using convolutional neural networks"
    - "rendering of eyes for eye shape registration and gaze estimation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "saliency prediction in the deep learning era successes limitations and future challenges"):
  with members:
    - "sg fcn a motion and memory based deep learning model for video saliency detection"
    - "faster gaze prediction with dense networks and fisher pruning"
    - "deep visual attention prediction"
    - "charades ego a large scale dataset of paired third and first person videos"
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "learning a saliency evaluation metric using crowdsourced perceptual judgments"
    - "deepvs a deep learning based video saliency prediction approach"

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)