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: "streaming video over http with consistent quality"
  Abstract: In conventional HTTP-based adaptive streaming (HAS), a video source is encoded at multiple levels of constant bitrate representations, and a client makes its representation selections according to the measured network bandwidth. While greatly simplifying adaptation to the varying network conditions, this strategy is not the best for optimizing the video quality experienced by end users. Quality fluctuation can be reduced if the natural variability of video content is taken into consideration. In this work, we study the design of a client rate adaptation algorithm to yield consistent video quality. We assume that clients have visibility into incoming video within a finite horizon. We also take advantage of the client-side video buffer, by using it as a breathing room for not only network bandwidth variability, but also video bitrate variability. The challenge, however, lies in how to balance these two variabilities to yield consistent video quality without risking a buffer underrun. We propose an optimization solution that uses an online algorithm to adapt the video bitrate step-by-step, while applying dynamic programming at each step. We incorporate our solution into PANDA -- a practical rate adaptation algorithm designed for HAS deployment at scale.

Connected through hyperedge HE-1 (induced by source paper "qoe oriented resource allocation for 360 degree video transmission over heterogeneous networks"):
  with members:
    - "automatic salient object detection for panoramic images using region growing and fixation prediction model"
    - "salnet360 saliency maps for omni directional images with cnn"
    - "a rate adaptation algorithm for tile based 360 degree video streaming"
    - "how do people explore virtual environments"
    - "bola near optimal bitrate adaptation for online videos"
    - "adaptive 360 vr video streaming divide and conquer"
    - "non cooperative feedback rate control game for channel state information in wireless networks"

Connected through hyperedge HE-2 (induced by source paper "qoe based low delay live streaming using throughput predictions"):
  with members:
    - "a control theoretic approach to adaptive video streaming in dense wireless networks"
    - "adaptive video streaming for wireless networks with multiple users and helpers"
    - "probe and adapt rate adaptation for http video streaming at scale"

Connected through hyperedge HE-3 (induced by source paper "optimal selection of adaptive streaming representations"):
  with members:
    - "using the buffer to avoid rebuffers evidence from a large video streaming service"
    - "probe and adapt rate adaptation for http video streaming at scale"
    - "nova qoe driven optimization of dash based video delivery in networks"
    - "rate model for compressed video considering impacts of spatial temporal and amplitude resolutions and its applications for video coding and adaptation"

Connected through hyperedge HE-4 (induced by source paper "optimized packet scheduling in multiview video navigation systems"):
  with members:
    - "modeling the time varying subjective quality of http video streams with rate adaptations"
    - "correlation aware packet scheduling in multi camera networks"

Connected through hyperedge HE-5 (induced by source paper "enhancing user experience for multi screen social tv streaming over wireless networks"):
  with members:
    - "nova qoe driven optimization of dash based video delivery in networks"

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.NI(Networking and Internet Architecture)