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: "continual learning improves internet video streaming"
  Abstract: We describe Fugu, a continual learning algorithm for bitrate selection in streaming video. Each day, Fugu retrains a neural network from its experience in deployment over the prior week. The neural network predicts how long it would take to transfer each available version of the upcoming video chunks, given recent history and internal TCP statistics. #R##N#We evaluate Fugu with Puffer, a public website we built that streams live TV using Fugu and existing algorithms. Over a nine-day period in January 2019, Puffer streamed 8,131 hours of video to 3,719 unique users. Compared with buffer-based control, MPC, RobustMPC, and Pensieve, Fugu performs better on several metrics: it stalls 5-13x less and has better and more stable video quality, and users who were randomly assigned to Fugu streamed for longer on average before quitting or reloading. We find that TCP statistics can aid bitrate selection, that congestion-control and bitrate-selection algorithms are best selected jointly, that probabilistic transfer-time estimates that consider chunk size outperform point estimates of throughput, and that both stalls and video quality have an influence on how long users choose to keep a video stream playing. #R##N#Fugu's results suggest that continual learning of network algorithms in situ is a promising area of research. To support further investigation, we plan to operate Puffer for several years and will open it to researchers for evaluating new congestion-control and bitrate-selection approaches.

Connected through hyperedge HE-1 (induced by source paper "reinforcement learning for bandwidth estimation and congestion control in real time communications"):
  with members:
    - "mvfst rl an asynchronous rl framework for congestion control with delayed actions"
    - "qflow a reinforcement learning approach to high qoe video streaming over wireless networks"
    - "iroko a framework to prototype reinforcement learning for data center traffic control"
    - "qarc video quality aware rate control for real time video streaming based on deep reinforcement learning"
    - "proximal policy optimization algorithms"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

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)