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: "kolmogorov s structure functions and model selection"
  Abstract: In 1974 Kolmogorov proposed a non-probabilistic approach to statistics and model selection. Let data be finite binary strings and models be finite sets of binary strings. Consider model classes consisting of models of given maximal (Kolmogorov) complexity. The ``structure function'' of the given data expresses the relation between the complexity level constraint on a model class and the least log-cardinality of a model in the class containing the data. We show that the structure function determines all stochastic properties of the data: for every constrained model class it determines the individual best-fitting model in the class irrespective of whether the ``true'' model is in the model class considered or not. In this setting, this happens {\em with certainty}, rather than with high probability as is in the classical case. We precisely quantify the goodness-of-fit of an individual model with respect to individual data. We show that--within the obvious constraints--every graph is realized by the structure function of some data. We determine the (un)computability properties of the various functions contemplated and of the ``algorithmic minimal sufficient statistic.''

Connected through hyperedge HE-1 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning"):
  with members:
    - "learning representations for neural network based classification using the information bottleneck principle"
    - "dynamics aware unsupervised discovery of skills"
    - "causal confusion in imitation learning"
    - "unsupervised discovery of temporal structure in noisy data with dynamical components analysis"
    - "learnability for the information bottleneck"
    - "pareto optimal data compression for binary classification tasks"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "the conditional entropy bottleneck"

Connected through hyperedge HE-2 (induced by source paper "information kernels"):
  with members:
    - "algorithmic statistics forty years later"
    - "algorithmic statistics revisited"
    - "all sampling methods produce outliers"
    - "rate distortion and denoising of individual data using kolmogorov complexity"

Connected through hyperedge HE-3 (induced by source paper "the information complexity of learning tasks their structure and their distance"):
  with members:
    - "the dynamics of differential learning i information dynamics and task reachability"
    - "taskonomy disentangling task transfer learning"
    - "domain adaptation for visual applications a comprehensive survey"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "emergence of invariance and disentanglement in deep representations"
    - "critical learning periods in deep neural networks"
    - "new insights and perspectives on the natural gradient method"
    - "a pac bayesian tutorial with a dropout bound"

Connected through hyperedge HE-4 (induced by source paper "dynamics and reachability of learning tasks"):
  with members:
    - "task2vec task embedding for meta learning"
    - "the information complexity of learning tasks their structure and their distance"
    - "taskonomy disentangling task transfer learning"
    - "emergence of invariance and disentanglement in deep representations"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "new insights and perspectives on the natural gradient method"
    - "a pac bayesian tutorial with a dropout bound"

Connected through hyperedge HE-5 (induced by source paper "on the algorithmic probability of sets"):
  with members:
    - "algorithmic statistics forty years later"
    - "algorithmic statistics revisited"
    - "all sampling methods produce outliers"

Connected through hyperedge HE-6 (induced by source paper "on the complexity of completing binary predicates"):
  with members:
    - "algorithmic statistics forty years later"
    - "algorithmic statistics revisited"
    - "all sampling methods produce outliers"

Connected through hyperedge HE-7 (induced by source paper "kolmogorov complexity in the ussr 1975 1982 isolation and its end"):
  with members:
    - "some theorems on the algorithmic approach to probability theory and information theory"

Connected through hyperedge HE-8 (induced by source paper "recursia rrt recursive translatable point set pattern discovery with removal of redundant translators"):
  with members:
    - "minimum description length induction bayesianism and kolmogorov complexity"

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.CC(Computational Complexity)