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: "predicting head movement in panoramic video a deep reinforcement learning approach"
  Abstract: Panoramic video provides immersive and interactive experience by enabling humans to control the field of view (FoV) through head movement (HM). Thus, HM plays a key role in modeling human attention on panoramic video. This paper establishes a database collecting subjects’ HM in panoramic video sequences. From this database, we find that the HM data are highly consistent across subjects. Furthermore, we find that deep reinforcement learning (DRL) can be applied to predict HM positions, via maximizing the reward of imitating human HM scanpaths through the agent's actions. Based on our findings, we propose a DRL-based HM prediction (DHP) approach with offline and online versions, called offline-DHP and online-DHP. In offline-DHP, multiple DRL workflows are run to determine potential HM positions at each panoramic frame. Then, a heat map of the potential HM positions, named the HM map, is generated as the output of offline-DHP. In online-DHP, the next HM position of one subject is estimated given the currently observed HM position, which is achieved by developing a DRL algorithm upon the learned offline-DHP model. Finally, the experiments validate that our approach is effective in both offline and online prediction of HM positions for panoramic video, and that the learned offline-DHP model can improve the performance of online-DHP.

Connected through hyperedge HE-1 (induced by source paper "a fixation based 360 benchmark dataset for salient object detection"):
  with members:
    - "global context aware progressive aggregation network for salient object detection"
    - "distortion adaptive salient object detection in 360 circ omnidirectional images"
    - "rethinking rgb d salient object detection models datasets and large scale benchmarks"
    - "a simple pooling based design for real time salient object detection"
    - "cascaded partial decoder for fast and accurate salient object detection"
    - "egnet edge guidance network for salient object detection"
    - "enhanced alignment measure for binary foreground map evaluation"
    - "bridge the gap between vqa and human behavior on omnidirectional video a large scale dataset and a deep learning model"

Connected through hyperedge HE-2 (induced by source paper "prediction communication and computing duration optimization for vr video streaming"):
  with members:
    - "very long term field of view prediction for 360 degree video streaming"
    - "optimal multicast of tiled 360 vr video in ofdma systems"
    - "communications caching and computing for mobile virtual reality modeling and tradeoff"
    - "a survey on mobile edge computing the communication perspective"
    - "virtual reality over wireless networks quality of service model and learning based resource management"
    - "adaptive 360 vr video streaming divide and conquer"

Connected through hyperedge HE-3 (induced by source paper "distortion adaptive salient object detection in 360 circ omnidirectional images"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "cube padding for weakly supervised saliency prediction in 360 deg videos"
    - "reverse attention for salient object detection"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "deep 360 pilot learning a deep agent for piloting through 360 deg sports video"
    - "picanet learning pixel wise contextual attention for saliency detection"
    - "amulet aggregating multi level convolutional features for salient object detection"
    - "deep saliency with encoded low level distance map and high level features"

Connected through hyperedge HE-4 (induced by source paper "saliency prediction on omnidirectional images with generative adversarial imitation learning"):
  with members:
    - "deep visual attention prediction"
    - "bridge the gap between vqa and human behavior on omnidirectional video a large scale dataset and a deep learning model"
    - "saltinet scan path prediction on 360 degree images using saliency volumes"
    - "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-5 (induced by source paper "revisiting deep architectures for head motion prediction in 360 videos"):
  with members:
    - "spherical clustering of users navigating 360 degree content"
    - "the prefetch aggressiveness tradeoff in 360 video streaming"
    - "predicting gaze in egocentric video by learning task dependent attention transition"
    - "rate utility optimized streaming of volumetric media for augmented reality"
    - "predicting deeper into the future of semantic segmentation"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "making 360 circ video watchable in 2d learning videography for click free viewing"
    - "on human motion prediction using recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "very long term field of view prediction for 360 degree video streaming"):
  with members:
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "granger causal attentive mixtures of experts learning important features with neural networks"
    - "effective approaches to attention based neural machine translation"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-7 (induced by source paper "a taxonomy and dataset for 360 videos"):
  with members:
    - "spherical clustering of users navigating 360 degree content"
    - "the prefetch aggressiveness tradeoff in 360 video streaming"
    - "movie editing and cognitive event segmentation in virtual reality video"
    - "viewport adaptive navigable 360 degree video delivery"

Connected through hyperedge HE-8 (induced by source paper "probabilistic tile visibility based server side rate adaptation for adaptive 360 degree video streaming"):
  with members:
    - "viewport adaptive navigable 360 degree video delivery"
    - "a rate adaptation algorithm for tile based 360 degree video streaming"
    - "adam a method for stochastic optimization"
    - "probe and adapt rate adaptation for http video streaming at scale"

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)