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 "kolmogorov complexity version of slepian wolf coding"):
  Abstract: Alice and Bob are given two correlated n-bit strings x_1 and, respectively, x_2, which they want to losslessly compress and send to Zack. They can either collaborate by sharing their strings, or work separately. We show that there is no disadvantage in the second scenario: Alice and Bob, without knowing the other party's string, can achieve almost optimal compression in the sense of Kolmogorov complexity. Furthermore, compression takes polynomial time and can be made at any combination of lengths that satisfy some necessary conditions (modulo additive polylog terms). More precisely, there exist probabilistic algorithms E_1, E_2, and deterministic algorithm D, with E_1 and E_2 running in polynomial time, having the following behavior: if n_1, n_2 are two integers satisfying n_1 + n_2 \geq C(x_1,x_2), n_1 \geq C(x_1 | x_2), n_2 \geq C(x_2 | x_1), then for i \in {1,2}, E_i on input x_i and n_i outputs a string of length n_i + \polylog n such that D on input E_1(x_1), E_2(x_2) reconstructs (x_1,x_2) with high probability (where C(x) denotes the plain Kolmogorov complexity of x, and C(x \mid y) is the complexity of x conditioned by y). Our main result is more general, as it deals with the compression of any constant number of correlated strings. It is an analog in the framework of algorithmic information theory of the classic Slepian-Wolf Theorem, a fundamental result in network information theory, in which x_1 and x_2 are realizations of two discrete random variables formed by drawing independently n times from a joint distribution. Also, in the classical result, the decompressor needs to know the joint distribution of the sources. In our result no type of independence is assumed and the decompressor does not have any apriori information about the sources that are compressed, and it still is the case that distributed compression is on a par with centralized compression.

Includes members:
  Member "coding in the fork network in the framework of kolmogorov complexity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "universal almost optimal compression and slepian wolf coding in probabilistic polynomial time")
  Member "short lists with short programs in short time":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "how incomputable is kolmogorov complexity")
  Member "short lists with short programs in short time a short proof":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "how incomputable is kolmogorov complexity")
  Member "linear list approximation for short programs or the power of a few random bits":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "universal almost optimal compression and slepian wolf coding in probabilistic polynomial time")
  Member "short lists for shortest descriptions in short time":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "universal almost optimal compression and slepian wolf coding in probabilistic polynomial time")
  Member "complexity of complexity and strings with maximal plain and prefix kolmogorov complexity":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "how incomputable is kolmogorov complexity")
  Member "variations on muchnik s conditional complexity theorem":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "universal almost optimal compression and slepian wolf coding in probabilistic polynomial time")

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)