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: "bola near optimal bitrate adaptation for online videos"
  Abstract: Modern video players employ complex algorithms to adapt the bitrate of the video that is shown to the user. Bitrate adaptation requires a tradeoff between reducing the probability that the video freezes and enhancing the quality of the video shown to the user. A bitrate that is too high leads to frequent video freezes (i.e., rebuffering), while a bitrate that is too low leads to poor video quality. Video providers segment the video into short chunks and encode each chunk at multiple bitrates. The video player adaptively chooses the bitrate of each chunk that is downloaded, possibly choosing different bitrates for successive chunks. While bitrate adaptation holds the key to a good quality of experience for the user, current video players use ad-hoc algorithms that are poorly understood. We formulate bitrate adaptation as a utility maximization problem and devise an online control algorithm called BOLA that uses Lyapunov optimization techniques to minimize rebuffering and maximize video quality. We prove that BOLA achieves a time-average utility that is within an additive term O(1/V) of the optimal value, for a control parameter V related to the video buffer size. Further, unlike prior work, our algorithm does not require any prediction of available network bandwidth. We empirically validate our algorithm in a simulated network environment using an extensive collection of network traces. We show that our algorithm achieves near-optimal utility and in many cases significantly higher utility than current state-of-the-art algorithms. Our work has immediate impact on real-world video players and for the evolving DASH standard for video transmission.

Connected through hyperedge HE-1 (induced by source paper "understanding video streaming algorithms in the wild"):
  with members:
    - "learning in situ a randomized experiment in video streaming"
    - "performance characterization of a commercial video streaming service"
    - "which adaptation logic an objective and subjective performance evaluation of http based adaptive media streaming systems"
    - "a control theoretic approach to adaptive video streaming in dense wireless networks"
    - "probe and adapt rate adaptation for http video streaming at scale"

Connected through hyperedge HE-2 (induced by source paper "explaining deep learning based networked systems"):
  with members:
    - "neural packet classification"
    - "hyper sagnn a self attention based graph neural network for hypergraphs"
    - "tensorflow js machine learning for the web and beyond"
    - "pforest in network inference with random forests"
    - "interpreting and improving natural language processing in machines with natural language processing in the brain"
    - "fooling neural network interpretations via adversarial model manipulation"
    - "gnn explainer a tool for post hoc explanation of graph neural networks"
    - "mmalfm explainable recommendation by leveraging reviews and images"

Connected through hyperedge HE-3 (induced by source paper "comyco quality aware adaptive video streaming via imitation learning"):
  with members:
    - "guided meta policy search"
    - "an algorithmic perspective on imitation learning"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "qarc video quality aware rate control for real time video streaming based on deep reinforcement learning"
    - "variance reduction for reinforcement learning in input driven environments"
    - "tiyuntsong a self play reinforcement learning approach for abr video streaming"
    - "cross layer effects on training neural algorithms for video streaming"
    - "dart noise injection for robust imitation learning"

Connected through hyperedge HE-4 (induced by source paper "cba contextual quality adaptation for adaptive bitrate video streaming extended version"):
  with members:
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "probe and adapt rate adaptation for http video streaming at scale"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "a contextual bandit approach to personalized news article recommendation"
    - "an axiomatic theory of fairness in network resource allocation"

Connected through hyperedge HE-5 (induced by source paper "optimizing adaptive video streaming in mobile networks via online learning"):
  with members:
    - "classifying flows and buffer state for youtube s http adaptive streaming service in mobile networks"
    - "online convex optimization and no regret learning algorithms guarantees and applications"
    - "multi codec dash dataset"
    - "a comparative case study of http adaptive streaming algorithms in mobile networks"
    - "http adaptive streaming with indoors outdoors detection in mobile networks"
    - "probe and adapt rate adaptation for http video streaming at scale"

Connected through hyperedge HE-6 (induced by source paper "learning in situ a randomized experiment in video streaming"):
  with members:
    - "challenges of real world reinforcement learning"
    - "variance reduction for reinforcement learning in input driven environments"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "probe and adapt rate adaptation for http video streaming at scale"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

Connected through hyperedge HE-7 (induced by source paper "pano optimizing 360 deg video streaming with a better understanding of quality perception"):
  with members:
    - "yolov3 an incremental improvement"
    - "viewport adaptive navigable 360 degree video delivery"
    - "adaptive 360 vr video streaming divide and conquer"
    - "high speed tracking with kernelized correlation filters"

Connected through hyperedge HE-8 (induced by source paper "a knowledge driven quality of experience model for adaptive streaming videos"):
  with members:
    - "qarc video quality aware rate control for real time video streaming based on deep reinforcement learning"
    - "learning to predict streaming video qoe distortions rebuffering and memory"
    - "optimal selection of adaptive streaming representations"
    - "probe and adapt rate adaptation for http video streaming at scale"

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)