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 "bounded turing reductions and data processing inequalities for sequences"):
  Abstract: A data processing inequality states that the quantity of shared information between two entities (e.g. signals, strings) cannot be significantly increased when one of the entities is processed by certain kinds of transformations. In this paper, we prove several data processing inequalities for sequences, where the transformations are bounded Turing functionals and the shared information is measured by the lower and upper mutual dimensions between sequences. #R##N#We show that, for all sequences $X,Y,$ and $Z$, if $Z$ is computable Lipschitz reducible to $X$, then \[ mdim(Z:Y) \leq mdim(X:Y) \text{ and } Mdim(Z:Y) \leq Mdim(X:Y). \] We also show how to derive different data processing inequalities by making adjustments to the computable bounds of the use of a Turing functional. #R##N#The yield of a Turing functional $\Phi^S$ with access to at most $n$ bits of the oracle $S$ is the smallest input $m \in \mathbb{N}$ such that $\Phi^{S \upharpoonright n}(m)\uparrow$. We show how to derive reverse data processing inequalities (i.e., data processing inequalities where the transformation may significantly increase the shared information between two entities) for sequences by applying computable bounds to the yield of a Turing functional.

Includes members:
  Member "mutual dimension and random sequences":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "algorithmic information plane kakeya sets and conditional dimension")
  Member "the dimensions of individual strings and sequences":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "who asked us how the theory of computing answers questions about analysis")

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.CC(Computational Complexity)