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 "asymptotics of discrete mdl for online prediction"):
  Abstract: Minimum description length (MDL) is an important principle for induction and prediction, with strong relations to optimal Bayesian learning. This paper deals with learning processes which are independent and identically distributed (i.i.d.) by means of two-part MDL, where the underlying model class is countable. We consider the online learning framework, i.e., observations come in one by one, and the predictor is allowed to update its state of mind after each time step. We identify two ways of predicting by MDL for this setup, namely, a static and a dynamic one. (A third variant, hybrid MDL, will turn out inferior.) We will prove that under the only assumption that the data is generated by a distribution contained in the model class, the MDL predictions converge to the true values almost surely. This is accomplished by proving finite bounds on the quadratic, the Hellinger, and the Kullback-Leibler loss of the MDL learner, which are, however, exponentially worse than for Bayesian prediction. We demonstrate that these bounds are sharp, even for model classes containing only Bernoulli distributions. We show how these bounds imply regret bounds for arbitrary loss functions. Our results apply to a wide range of setups, namely, sequence prediction, pattern classification, regression, and universal induction in the sense of algorithmic information theory among others.

Includes members:
  Member "adaptive online prediction by following the perturbed leader":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online non convex learning following the perturbed leader is optimal")
  Member "convergence of discrete mdl for sequential prediction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "open problems in universal induction intelligence")
  Member "on the convergence speed of mdl predictions for bernoulli sequences":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "open problems in universal induction intelligence")
  Member "sequence prediction based on monotone complexity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "open problems in universal induction intelligence")
  Member "convergence and loss bounds for bayesian sequence prediction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ultimate intelligence part i physical completeness and objectivity of induction")
  Member "the similarity metric":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "max margin class imbalanced learning with gaussian affinity")
  Member "minimum description length induction bayesianism and kolmogorov complexity":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "recursia rrt recursive translatable point set pattern discovery with removal of redundant translators")
  Member "new error bounds for solomonoff prediction":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "bad universal priors and notions of optimality")

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.IT(Information Theory)