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 "constant regret generalized mixability and mirror descent"):
  Abstract: We consider the setting of prediction with expert advice; a learner makes predictions by aggregating those of a group of experts. Under this setting, and with the right choice of loss function and "mixing" algorithm, it is possible for the learner to achieve constant regret regardless of the number of prediction rounds. For example, constant regret can be achieved with \emph{mixable} losses using the \emph{Aggregating Algorithm} (AA). The \emph{Generalized Aggregating Algorithm} (GAA) is a name for a family of algorithms parameterized by convex functions on simplices (entropies), which reduce to the AA when using the \emph{Shannon entropy}. For a given entropy $\Phi$, losses for which constant regret is possible using the GAA are called $\Phi$-mixable. Which losses are $\Phi$-mixable was previously left as an open question. We fully characterize $\Phi$-mixability, and answer other open questions posed by \cite{Reid2015}. We also elaborate on the tight link between the GAA and the \emph{mirror descent algorithm} which minimizes the weighted loss of experts.

Includes members:
  Member "exp concavity of proper composite losses":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "proper composite loss functions in arbitrary dimensions")
  Member "generalized mixability via entropic duality":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning games and rademacher observations losses")

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)