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 "fast rates for general unbounded loss functions from erm to generalized bayes"):
  Abstract: We present new excess risk bounds for general unbounded loss functions including log loss and squared loss, where the distribution of the losses may be heavy-tailed. The bounds hold for general estimators, but they are optimized when applied to $\eta$-generalized Bayesian, MDL, and ERM estimators. When applied with log loss, the bounds imply convergence rates for generalized Bayesian inference under misspecification in terms of a generalization of the Hellinger metric as long as the learning rate $\eta$ is set correctly. For general loss functions, our bounds rely on two separate conditions: the $v$-GRIP (generalized reversed information projection) conditions, which control the lower tail of the excess loss; and the newly introduced witness condition, which controls the upper tail. The parameter $v$ in the $v$-GRIP conditions determines the achievable rate and is akin to the exponent in the well-known Tsybakov margin condition and the Bernstein condition for bounded losses, which the $v$-GRIP conditions generalize; favorable $v$ in combination with small model complexity leads to $\tilde{O}(1/n)$ rates. The witness condition allows us to connect the excess risk to an 'annealed' version thereof, by which we generalize several previous results connecting Hellinger and R\'enyi divergence to KL divergence.

Includes members:
  Member "a tight excess risk bound via a unified pac bayesian rademacher shtarkov mdl complexity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the generalization of bayesian deep nets for multi class classification")
  Member "combining adversarial guarantees and stochastic fast rates in online learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fast rates for online prediction with abstention")
  Member "fast rates in statistical and online learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "strength from weakness fast learning using weak supervision")
  Member "learning without concentration":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning with correntropy induced losses for regression with mixture of symmetric stable noise")
  Member "from stochastic mixability to fast rates":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "pac bayes un expected bernstein inequality")
  Member "renyi divergence and kullback leibler divergence":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "minimax analysis of active learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adaptive region based active learning")
  Member "follow the leader if you can hedge if you must":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "fast rates for online prediction with abstention")

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)