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 "increasing the quality of 360 video streaming by transitioning between viewport quality adaptation mechanisms"):
  Abstract: Virtual reality has been gaining popularity in recent years caused by the proliferation of affordable consumer-grade devices such as Oculus Rift, HTC Vive, and Samsung VR. Amongst the various VR applications, 360{\deg} video streaming is currently one of the most popular ones. It allows user to change their field-of-view (FoV) based on head movement, which enables them to freely select an area anywhere from the sphere the video is (virtually) projected to. While 360{\deg} video streaming offers new exciting ways of consuming content for viewers, it poses a series of challenges to the systems that are responsible for the distribution of such content from the origin to the viewer. One challenge is the significantly increased bandwidth requirement for streaming such content in real time. Recent research has shown that only streaming the content that is in the user's FoV in high quality can lead to strong bandwidth savings. This can be achieved by analyzing the viewers head orientation and movement based on sensor information. Alternatively, historic information from users that watched the content in the past can be taken into account to prefetch 360{\deg} video data in high quality assuming the viewer will direct the FoV to these areas. In this paper, we present a 360{\deg} video streaming system that transitions between sensor- and content-based predictive mechanisms. We evaluate the effects of this transition-based approach on the Quality of Experience (QoE) of such a VR streaming system and show that the perceived quality can be increased between 50\% and 80\% compared to systems that only apply either one of the two approaches.

Includes members:
  Member "viewport driven rate distortion optimized 360 deg video streaming":
    also connected through other hyperedges:
      (none)
  Member "viewport adaptive navigable 360 degree video delivery":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "coco gan generation by parts via conditional coordinating")
  Member "streaming virtual reality content":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "visual question answering on 360 deg images")

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.NI(Networking and Internet Architecture)