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 "360 degree video gaze behaviour a ground truth data set and a classification algorithm for eye movements"):
  Abstract: Eye tracking and the analysis of gaze behaviour are established tools to produce insights into how humans observe their surroundings and consume visual multimedia content. For example, gaze recordings may be directly used to study attention allocation towards the areas and objects of interest. Furthermore, segmenting the raw gaze traces into their constituent eye movements has applications in the assessment of subjective quality and mental load, and may improve computational saliency prediction of the content as well. Currently, eye trackers are beginning to be integrated into commodity virtual and augmented reality set-ups that allow for more diverse stimuli to be presented, including 360-degree content. However, because of the more complex eye-head coordination patterns that emerge, the definitions and the well-established methods that were developed for monitor-based eye tracking are often no longer directly applicable. The main contributions of this work to the field of 360-degree content analysis are threefold: First, we collect and partially annotate a new eye tracking data set for naturalistic 360-degree videos. Second, we propose a new two-stage pipeline for reliable manual annotation of both "traditional" (fixations and saccades) and more complex eye movement types that is implemented in a flexible user interface. Lastly, we develop and test a proof-of-concept algorithm for automatic classification of all the eye movement types in our data set. The data set and the source code for both the annotation tool and the algorithm are publicly available at https://gin.g-node.org/ioannis.agtzidis/360_em_dataset.

Includes members:
  Member "cube padding for weakly supervised saliency prediction in 360 deg videos":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "spherical image generation from a single normal field of view image by considering scene symmetry")
  Member "bayesian identification of fixations saccades and smooth pursuits":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "gaze in wild a dataset for studying eye and head coordination in everyday activities")
  Member "learning to compare image patches via convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "image matching across wide baselines from paper to practice")

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.MM(Multimedia)