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: "algorithmic statistics normal objects and universal models"
  Abstract: Kolmogorov suggested to measure quality of a statistical hypothesis $P$ for a data $x$ by two parameters: Kolmogorov complexity $C(P)$ of the hypothesis and the probability $P(x)$ of $x$ with respect to $P$. #R##N#P. G\'acs, J. Tromp, P.M.B. Vit\'anyi discovered a small class of models that are universal in the following sense. Each hypothesis $S_{ij}$ from that class is identified by two integer parameters $i,j$ and for every data $x$ and for each complexity level $\alpha$ there is a hypothesis $S_{ij}$ with $j\le i\le l(x)$ of complexity at most $\alpha$ that has almost the best fit among all hypotheses of complexity at most $\alpha$. The hypothesis $S_{ij}$ is identified by $i$ and the leading $i-j$ bits of the binary representation of the number of strings of complexity at most $i$. On the other hand, the initial data $x$ might be completely irrelevant to the the number of strings of complexity at most $i$. Thus $S_{ij}$ seems to have some information irrelevant to the data, which undermines Kolmogorov's approach: the best hypotheses should not have irrelevant information. #R##N#To restrict the class of hypotheses for a data $x$ to those that have only relevant information, Vereshchagin introduced a notion of a strong model for $x$: those are models for $x$ whose total conditional complexity conditional to $x$ is negligible. An object $x$ is called normal if for each complexity level $\alpha$ at least one its best fitting model of that complexity is strong. #R##N#In this paper we show that there are "many types" of normal strings. Our second result states that there is a normal object $x$ such that all its best fitting models $S_{ij}$ are not strong for $x$. Our last result states that every best fit strong model for a normal object is again a normal object.

Connected through hyperedge HE-1 (induced by source paper "algorithmic statistics forty years later"):
  with members:
    - "around kolmogorov complexity basic notions and results"
    - "algorithmic statistics revisited"
    - "algorithmic statistics prediction and machine learning"
    - "some properties of antistochastic strings"
    - "sophistication vs logical depth"
    - "depth as randomness deficiency"
    - "rate distortion and denoising of individual data using kolmogorov complexity"
    - "kolmogorov s structure functions and model selection"

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.IT(Information Theory)