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: "using the buffer to avoid rebuffers evidence from a large video streaming service"
  Abstract: To provide a better streaming experience, video clients today select their video rates by observing and estimating the available capacity. Recent work has shown that capacity estimation is fraught with difficulties because of complex interactions between the ABR control loop, HTTP server performance and TCP congestion control. Estimation-based rate selection algorithms can lead to unnecessary rebuffering events and suboptimal video quality. This paper argues that we should do away with estimating network capacity, and instead directly observe and control the playback buffer--which is the state variable we are most interested in controlling. We present a class of "buffer-based" rate selection algorithms that reduce the rebuffering rate while allowing us to control the delivered video quality. We implemented our algorithms inside the Netflix video client and ran a series of experiments spanning millions of Netflix users around the world. Our results show that by doing away with estimating network capacity and instead focusing on buffer occupancy, we can reduce rebuffer rates by 20% while holding video rate constant.

Connected through hyperedge HE-1 (induced by source paper "optimal selection of adaptive streaming representations"):
  with members:
    - "streaming video over http with consistent quality"
    - "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"

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)