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: "optimality of universal bayesian sequence prediction for general loss and alphabet"
  Abstract: Various optimality properties of universal sequence predictors based on Bayes-mixtures in general, and Solomonoff's prediction scheme in particular, will be studied. The probability of observing $x_t$ at time $t$, given past observations $x_1...x_{t-1}$ can be computed with the chain rule if the true generating distribution $\mu$ of the sequences $x_1x_2x_3...$ is known. If $\mu$ is unknown, but known to belong to a countable or continuous class $\M$ one can base ones prediction on the Bayes-mixture $\xi$ defined as a $w_\nu$-weighted sum or integral of distributions $\nu\in\M$. The cumulative expected loss of the Bayes-optimal universal prediction scheme based on $\xi$ is shown to be close to the loss of the Bayes-optimal, but infeasible prediction scheme based on $\mu$. We show that the bounds are tight and that no other predictor can lead to significantly smaller bounds. Furthermore, for various performance measures, we show Pareto-optimality of $\xi$ and give an Occam's razor argument that the choice $w_\nu\sim 2^{-K(\nu)}$ for the weights is optimal, where $K(\nu)$ is the length of the shortest program describing $\nu$. The results are applied to games of chance, defined as a sequence of bets, observations, and rewards. The prediction schemes (and bounds) are compared to the popular predictors based on expert advice. Extensions to infinite alphabets, partial, delayed and probabilistic prediction, classification, and more active systems are briefly discussed.

Connected through hyperedge HE-1 (induced by source paper "thompson sampling for adversarial bit prediction"):
  with members:
    - "first order regret analysis of thompson sampling"
    - "introduction to multi armed bandits"
    - "a tutorial on thompson sampling"
    - "towards optimal algorithms for prediction with expert advice"
    - "an information theoretic analysis of thompson sampling"
    - "further optimal regret bounds for thompson sampling"
    - "the best of both worlds stochastic and adversarial bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-2 (induced by source paper "the foundations of deep learning with a path towards general intelligence"):
  with members:
    - "progressive neural networks"
    - "a simple neural network module for relational reasoning"
    - "dynamic routing between capsules"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "neural task programming learning to generalize across hierarchical tasks"
    - "geometric deep learning going beyond euclidean data"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "imagination augmented agents for deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "offline to online conversion"):
  with members:
    - "indefinitely oscillating martingales"
    - "on sequence prediction for arbitrary measures"
    - "asymptotics of discrete mdl for online prediction"

Connected through hyperedge HE-4 (induced by source paper "concentration and confidence for discrete bayesian sequence predictors"):
  with members:
    - "convergence and error bounds for universal prediction of nonbinary sequences"

Connected through hyperedge HE-5 (induced by source paper "a philosophical treatise of universal induction"):
  with members:
    - "a complete theory of everything will be subjective"
    - "a monte carlo aixi approximation"
    - "open problems in universal induction intelligence"
    - "algorithmic information theory a brief non technical guide to the field"
    - "algorithmic complexity bounds on future prediction errors"
    - "universal intelligence a definition of machine intelligence"
    - "similarity of objects and the meaning of words"
    - "adaptive online prediction by following the perturbed leader"

Connected through hyperedge HE-6 (induced by source paper "open problems in universal induction intelligence"):
  with members:
    - "policy iteration for factored mdps"
    - "feature dynamic bayesian networks"
    - "online planning algorithms for pomdps"
    - "feature markov decision processes"
    - "on the possibility of learning in reactive environments with arbitrary dependence"
    - "a collection of definitions of intelligence"
    - "universal algorithmic intelligence a mathematical top down approach"
    - "tests of machine intelligence"

Connected through hyperedge HE-7 (induced by source paper "on the possibility of learning in reactive environments with arbitrary dependence"):
  with members:
    - "on sequence prediction for arbitrary measures"
    - "general discounting versus average reward"
    - "defensive universal learning with experts"
    - "universal learning of repeated matrix games"
    - "prediction with expert advice by following the perturbed leader for general weights"
    - "self optimizing and pareto optimal policies in general environments based on bayes mixtures"

Connected through hyperedge HE-8 (induced by source paper "a minimum relative entropy principle for learning and acting"):
  with members:
    - "model based bayesian exploration"
    - "a bayesian rule for adaptive control based on causal interventions"
    - "defensive universal learning with experts"
    - "self optimizing and pareto optimal policies in general environments based on bayes mixtures"

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)