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 "algorithmic rate distortion theory"):
  Abstract: Kolmogorov complexity is the accepted absolute measure of the information content of an individual finite object. It gives the ultimate limit on the number of bits resulting from lossless compression of the object--more precisely, the number of bits from which effective lossless decompression of the object is possible. A similar absolute approach is needed for lossy compression, that is, a rate-distortion theory giving the ultimate effective limits for individual finite data objects. We give natural definitions of the rate-distortion functions of individual data (independent of a random source producing those data). We analyze the possible shapes of the rate-distortion graphs for all data and all computable distortions. We formulate a new problem related to the practice of lossy compression. Is it the case that a lossily compressed representation that realizes least distortion of the source object at a given rate also captures an appropriate part of the ``essence'' or ``meaning'' of that object? We are deliberately vague here until we introduce the appropriate formalism. Clearly, this question cannot be well posed in the Shannon setting, where we deal with expected distortion in communication, but explicitly not with ``meaning.'' We show that in our setting this question is answered in the affirmative for every distortion measure that satisfies a certain parsimony-of-covering property.

Includes members:
  Member "individual communication complexity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rate distortion and denoising of individual data using kolmogorov complexity")
  Member "kolmogorov s structure functions and model selection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")

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)