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 "optimal redundancy in computations from random oracles"):
  Abstract: A classic result in algorithmic information theory is that every infinite binary sequence is computable from a Martin-Loef random infinite binary sequence. Proved independently by Kucera and Gacs, this result answered a question by Charles Bennett and has seen numerous applications in the last 30 years. The optimal redundancy in such a coding process has, however, remained unknown. If the computation of the first n bits of a sequence requires n + g(n) bits of the random oracle, then g is the redundancy of the computation. Kucera implicitly achieved redundancy n log n while Gacs used a more elaborate block-coding procedure which achieved redundancy sqrt(n) log n. Different approaches to coding such as the one by Merkle and Mihailovic have not improved this redundancy bound. In this paper we devise a new coding method that achieves optimal logarithmic redundancy. Our redundancy bound is exponentially smaller than the previously best known bound and is known to be the best possible. It follows that redundancy r log n in computation from a random oracle is possible for every stream, if and only if r > 1.

Includes members:
  Member "forbidden information":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "compression of data streams down to their information content")
  Member "lower bounds on the redundancy in computations from random oracles via betting strategies with restricted wagers":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "granularity of wagers in games and the im possibility of savings")
  Member "every sequence is decompressible from a random one":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "compression of data streams down to their information content")

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)