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: "online convex optimization and no regret learning algorithms guarantees and applications"
  Abstract: Spurred by the enthusiasm surrounding the "Big Data" paradigm, the mathematical and algorithmic tools of online optimization have found widespread use in problems where the trade-off between data exploration and exploitation plays a predominant role. This trade-off is of particular importance to several branches and applications of signal processing, such as data mining, statistical inference, multimedia indexing and wireless communications (to name but a few). With this in mind, the aim of this tutorial paper is to provide a gentle introduction to online optimization and learning algorithms that are asymptotically optimal in hindsight - i.e., they approach the performance of a virtual algorithm with unlimited computational power and full knowledge of the future, a property known as no-regret. Particular attention is devoted to identifying the algorithms' theoretical performance guarantees and to establish links with classic optimization paradigms (both static and stochastic). To allow a better understanding of this toolbox, we provide several examples throughout the tutorial ranging from metric learning to wireless resource allocation problems.

Connected through hyperedge HE-1 (induced by source paper "cache optimization models and algorithms"):
  with members:
    - "learning to cache with no regrets"
    - "introduction to online convex optimization"
    - "cabaret leveraging recommendation systems for mobile edge caching"
    - "optimal and scalable caching for 5g using reinforcement learning of space time popularities"
    - "competitive caching with machine learned advice"
    - "show me the cache optimizing cache friendly recommendations for sequential content access"
    - "a reinforcement learning approach to proactive caching in wireless networks"
    - "jointly optimal routing and caching for arbitrary network topologies"

Connected through hyperedge HE-2 (induced by source paper "learning to cache with no regrets"):
  with members:
    - "optimal and scalable caching for 5g using reinforcement learning of space time popularities"
    - "competitive caching with machine learned advice"
    - "a reinforcement learning approach to proactive caching in wireless networks"
    - "jointly optimal routing and caching for arbitrary network topologies"
    - "spatial multi lru distributed caching for wireless networks with coverage overlaps"
    - "placing dynamic content in caches with small population"
    - "optimal geographic caching in cellular networks"
    - "adapting caching to audience retention rate which video chunk to store"

Connected through hyperedge HE-3 (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"
    - "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"
    - "probe and adapt rate adaptation for http video streaming at scale"

Connected through hyperedge HE-4 (induced by source paper "online learning for robust adaptive video streaming in mobile networks"):
  with members:
    - "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-5 (induced by source paper "learn2mac online learning multiple access for urllc applications"):
  with members:
    - "complexity of urllc scheduling and efficient approximation schemes"
    - "irregular repetition slotted aloha over the rayleigh block fading channel with capture"
    - "q csma queue length based csma ca algorithms for achieving maximum throughput and low delay in wireless networks"

Connected through hyperedge HE-6 (induced by source paper "pricing mechanism for resource sustainability in competitive online learning multi agent systems"):
  with members:
    - "learning in time varying games"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "trading regret for efficiency online convex optimization with long term constraints"

Connected through hyperedge HE-7 (induced by source paper "semi decentralized coordinated online learning for continuous games with coupled constraints via augmented lagrangian"):
  with members:
    - "nash and wardrop equilibria in aggregative games with coupling constraints"
    - "trading regret for efficiency online convex optimization with long term constraints"

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.LG(Machine Learning)