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: "probe and adapt rate adaptation for http video streaming at scale"
  Abstract: Today, the technology for video streaming over the Internet is converging towards a paradigm named HTTP-based adaptive streaming (HAS), which brings two new features. First, by using HTTP/TCP, it leverages network-friendly TCP to achieve both firewall/NAT traversal and bandwidth sharing. Second, by pre-encoding and storing the video in a number of discrete rate levels, it introduces video bitrate adaptivity in a scalable way so that the video encoding is excluded from the closed-loop adaptation. A conventional wisdom in HAS design is that since the TCP throughput observed by a client would indicate the available network bandwidth, it could be used as a reliable reference for video bitrate selection. We argue that this is no longer true when HAS becomes a substantial fraction of the total network traffic. We show that when multiple HAS clients compete at a network bottleneck, the discrete nature of the video bitrates results in difficulty for a client to correctly perceive its fair-share bandwidth. Through analysis and test bed experiments, we demonstrate that this fundamental limitation leads to video bitrate oscillation and other undesirable behaviors that negatively impact the video viewing experience. We therefore argue that it is necessary to design at the application layer using a "probe and adapt" principle for video bitrate adaptation (where "probe" refers to trial increment of the data rate, instead of sending auxiliary piggybacking traffic), which is akin, but also orthogonal to the transport-layer TCP congestion control. We present PANDA - a client-side rate adaptation algorithm for HAS - as a practical embodiment of this principle. Our test bed results show that compared to conventional algorithms, PANDA is able to reduce the instability of video bitrate selection by over 75% without increasing the risk of buffer underrun.

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"
    - "bola near optimal bitrate adaptation for online videos"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "fauras a proxy based framework for ensuring the fairness of adaptive video streaming over http 2 server push"):
  with members:
    - "a quantitative measure of fairness and discrimination for resource allocation in shared computer systems"

Connected through hyperedge HE-3 (induced by source paper "cba contextual quality adaptation for adaptive bitrate video streaming extended version"):
  with members:
    - "bola near optimal bitrate adaptation for online videos"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "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-4 (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"
    - "bola near optimal bitrate adaptation for online videos"

Connected through hyperedge HE-5 (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"
    - "bola near optimal bitrate adaptation for online videos"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

Connected through hyperedge HE-6 (induced by source paper "online learning for robust adaptive video streaming in mobile networks"):
  with members:
    - "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"

Connected through hyperedge HE-7 (induced by source paper "probabilistic tile visibility based server side rate adaptation for adaptive 360 degree video streaming"):
  with members:
    - "predicting head movement in panoramic video a deep reinforcement learning approach"
    - "viewport adaptive navigable 360 degree video delivery"
    - "a rate adaptation algorithm for tile based 360 degree video streaming"
    - "adam a method for stochastic optimization"

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"
    - "bola near optimal bitrate adaptation for online videos"
    - "optimal selection of adaptive streaming representations"

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)