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: "saltinet scan path prediction on 360 degree images using saliency volumes"
  Abstract: We introduce SaltiNet, a deep neural network for scanpath prediction trained on 360-degree images. The model is based on a temporal-aware novel representation of saliency information named the saliency volume. The first part of the network consists of a model trained to generate saliency volumes, whose parameters are fit by back-propagation computed from a binary cross entropy (BCE) loss over downsampled versions of the saliency volumes. Sampling strategies over these volumes are used to generate scanpaths over the 360-degree images. Our experiments show the advantages of using saliency volumes, and how they can be used for related tasks. Our source code and trained models available at this https URL

Connected through hyperedge HE-1 (induced by source paper "viewport aware deep reinforcement learning approach for 360 o video caching"):
  with members:
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "cost effective cache deployment in mobile heterogeneous networks"
    - "edge caching wireless networks performance analysis and optimization"
    - "deep reinforcement learning framework applications and embedded implementations"
    - "a deep reinforcement learning based framework for content caching"
    - "context aware proactive content caching with service differentiation in wireless networks"
    - "caching at the wireless edge design aspects challenges and future directions"
    - "deep reinforcement learning in large discrete action spaces"

Connected through hyperedge HE-2 (induced by source paper "saliency prediction on omnidirectional images with generative adversarial imitation learning"):
  with members:
    - "predicting head movement in panoramic video a deep reinforcement learning approach"
    - "deep visual attention prediction"
    - "bridge the gap between vqa and human behavior on omnidirectional video a large scale dataset and a deep learning model"
    - "deep 360 pilot learning a deep agent for piloting through 360 deg sports video"
    - "salnet360 saliency maps for omni directional images with cnn"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "a deep multi level network for saliency prediction"

Connected through hyperedge HE-3 (induced by source paper "predicting head movement in panoramic video a deep reinforcement learning approach"):
  with members:
    - "video salient object detection via fully convolutional networks"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "predicting the driver s focus of attention the dr eye ve project"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "asynchronous methods for deep reinforcement learning"
    - "two stream convolutional networks for dynamic saliency prediction"
    - "recurrent mixture density network for spatiotemporal visual attention"
    - "deepfix a fully convolutional neural network for predicting human eye fixations"

Connected through hyperedge HE-4 (induced by source paper "cube padding for weakly supervised saliency prediction in 360 deg videos"):
  with members:
    - "a deep ranking model for spatio temporal highlight detection from a 360 video"
    - "end to end saliency mapping via probability distribution prediction"
    - "viraliency pooling local virality"
    - "the 2017 davis challenge on video object segmentation"
    - "learning spherical convolution for fast features from 360 deg imagery"
    - "semantic driven generation of hyperlapse from 360 circ video"
    - "learning video object segmentation with visual memory"
    - "salnet360 saliency maps for omni directional images with cnn"

Connected through hyperedge HE-5 (induced by source paper "pathgan visual scanpath prediction with generative adversarial networks"):
  with members:
    - "end to end saliency mapping via probability distribution prediction"
    - "deep 360 pilot learning a deep agent for piloting through 360 deg sports video"
    - "a deep multi level network for saliency prediction"
    - "precomputed real time texture synthesis with markovian generative adversarial networks"
    - "eye tracking for everyone"
    - "energy based generative adversarial network"
    - "generative image modeling using style and structure adversarial networks"
    - "generative adversarial text to image synthesis"

Connected through hyperedge HE-6 (induced by source paper "saliency map estimation for omni directional image considering prior distributions"):
  with members:
    - "salnet360 saliency maps for omni directional images with cnn"
    - "deepgaze ii reading fixations from deep features trained on object recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "self view grounding given a narrated 360 deg video"):
  with members:
    - "discriminative bimodal networks for visual localization and detection with natural language queries"
    - "deep 360 pilot learning a deep agent for piloting through 360 deg sports video"
    - "weakly supervised visual grounding of phrases with linguistic structures"
    - "making 360 circ video watchable in 2d learning videography for click free viewing"
    - "grounding of textual phrases in images by reconstruction"
    - "leveraging video descriptions to learn video question answering"
    - "title generation for user generated videos"
    - "generation and comprehension of unambiguous object descriptions"

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)