human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "online learning for robust adaptive video streaming in mobile networks"):
  Abstract: In this paper, we propose a novel algorithm for video quality adaptation in HTTP Adaptive Streaming (HAS), based on Online Convex Optimization (OCO). The proposed algorithm, named Learn2Adapt (L2A), is shown to provide a robust adaptation strategy which, unlike most of the state-of-the-art techniques, does not require parameter tuning, channel model assumptions, or application-specific adjustments. These properties make it very suitable for mobile users, who typically experience fast variations in channel characteristics. Simulations show that L2A improves on average streaming bit-rate without impairing the overall Quality of Experience (QoE), a result that is independent of the channel and application scenarios.

Includes members:
  Member "online convex optimization and no regret learning algorithms guarantees and applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cache optimization models and algorithms")
  Member "multi codec dash dataset":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimizing adaptive video streaming in mobile networks via online learning")
  Member "a comparative case study of http adaptive streaming algorithms in mobile networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimizing adaptive video streaming in mobile networks via online learning")
  Member "http adaptive streaming with indoors outdoors detection in mobile networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimizing adaptive video streaming in mobile networks via online learning")
  Member "probe and adapt rate adaptation for http video streaming at scale":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "understanding video streaming algorithms in the wild")

Question: Please tell me which class the source paper 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.MM(Multimedia)