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 with feedback graphs and switching costs"):
  Abstract: We study online learning when partial feedback information is provided following every action of the learning process, and the learner incurs switching costs for changing his actions. In this setting, the feedback information system can be represented by a graph, and previous work provided the expected regret of the learner in the case of a clique (Expert setup), or disconnected single loops (Multi-Armed Bandits). We provide a lower bound on the expected regret in the partial information (PI) setting, namely for general feedback graphs ---excluding the clique. We show that all algorithms that are optimal without switching costs are necessarily sub-optimal in the presence of switching costs, which motivates the need to design new algorithms in this setup. We propose two novel algorithms: Threshold Based EXP3 and EXP3.SC. For the two special cases of symmetric PI setting and Multi-Armed-Bandits, we show that the expected regret of both algorithms is order optimal in the duration of the learning process with a pre-constant dependent on the feedback system. Additionally, we show that Threshold Based EXP3 is order optimal in the switching cost, whereas EXP3.SC is not. Finally, empirical evaluations show that Threshold Based EXP3 outperforms previous algorithm EXP3 SET in the presence of switching costs, and Batch EXP3 in the special setting of Multi-Armed Bandits with switching costs, where both algorithms are order optimal.

Includes members:
  Member "unifying the stochastic and the adversarial bandits with knapsack":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "advances in bandits with knapsacks")
  Member "bandits with movement costs and adaptive pricing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to bid without knowing your value")
  Member "multi armed bandits with metric movement costs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a successive elimination approach to adaptive robotic source seeking")
  Member "dcm bandits learning to rank with multiple clicks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep reinforcement learning for search recommendation and online advertising a survey by xiangyu zhao long xia jiliang tang and dawei yin with martin vesely as coordinator")
  Member "cascading bandits for large scale recommendation problems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "best arm identification for cascading bandits in the fixed confidence setting")
  Member "on independent sets in random graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the average case complexity of counting cliques in erdos renyi hypergraphs")
  Member "online learning with feedback graphs beyond bandits":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "regret minimization in stochastic contextual dueling bandits")
  Member "nonstochastic multi armed bandits with graph structured feedback":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "regret minimization in stochastic contextual dueling bandits")

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.LG(Machine Learning)