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: "variations on muchnik s conditional complexity theorem"
  Abstract: Muchnik's theorem about simple conditional descriptions states that for all strings $a$ and $b$ there exists a short program $p$ transforming $a$ to $b$ that has the least possible length and is simple conditional on $b$. In this paper we present two new proofs of this theorem. The first one is based on the on-line matching algorithm for bipartite graphs. The second one, based on extractors, can be generalized to prove a version of Muchnik's theorem for space-bounded Kolmogorov complexity.

Connected through hyperedge HE-1 (induced by source paper "universal almost optimal compression and slepian wolf coding in probabilistic polynomial time"):
  with members:
    - "coding in the fork network in the framework of kolmogorov complexity"
    - "kolmogorov complexity version of slepian wolf coding"
    - "short lists with short programs in short time"
    - "short lists with short programs in short time a short proof"
    - "linear list approximation for short programs or the power of a few random bits"
    - "short lists for shortest descriptions in short time"

Connected through hyperedge HE-2 (induced by source paper "secret key agreement from correlated data with no prior information"):
  with members:
    - "an operational characterization of mutual information in algorithmic information theory"
    - "on slepian wolf theorem with interaction"
    - "kolmogorov complexity version of slepian wolf coding"
    - "linear list approximation for short programs or the power of a few random bits"
    - "information equals amortized communication"

Connected through hyperedge HE-3 (induced by source paper "an operational characterization of mutual information in algorithmic information theory"):
  with members:
    - "optimal probabilistic polynomial time compression and the slepian wolf theorem tighter version and simple proofs"
    - "kolmogorov complexity version of slepian wolf coding"
    - "conditional information inequalities and combinatorial applications"
    - "linear list approximation for short programs or the power of a few random bits"
    - "conditional information inequalities for entropic and almost entropic points"
    - "common information and secret key capacity"
    - "common information revisited"
    - "optimal rate code constructions for computationally simple channels"

Connected through hyperedge HE-4 (induced by source paper "kolmogorov complexity version of slepian wolf coding"):
  with members:
    - "coding in the fork network in the framework of kolmogorov complexity"
    - "short lists with short programs in short time"
    - "short lists with short programs in short time a short proof"
    - "linear list approximation for short programs or the power of a few random bits"
    - "short lists for shortest descriptions in short time"
    - "complexity of complexity and strings with maximal plain and prefix kolmogorov complexity"

Connected through hyperedge HE-5 (induced by source paper "exact expression for information distance"):
  with members:
    - "information distance in multiples"
    - "the google similarity distance"
    - "the similarity metric"

Connected through hyperedge HE-6 (induced by source paper "short lists with short programs in short time"):
  with members:
    - "short lists with short programs in short time a short proof"
    - "space bounded kolmogorov extractors"
    - "nonuniform kolmogorov extractors"
    - "short lists for shortest descriptions in short time"
    - "complexity of complexity and strings with maximal plain and prefix kolmogorov complexity"
    - "possibilities and impossibilities in kolmogorov complexity extraction"
    - "symmetry of information and bounds on nonuniform randomness extraction via kolmogorov extractors"
    - "improving the space bounded version of muchnik s conditional complexity theorem via naive derandomization"

Connected through hyperedge HE-7 (induced by source paper "short lists with short programs in short time a short proof"):
  with members:
    - "short lists with short programs in short time"
    - "linear list approximation for short programs or the power of a few random bits"
    - "short lists for shortest descriptions in short time"

Connected through hyperedge HE-8 (induced by source paper "short lists for shortest descriptions in short time"):
  with members:
    - "short lists with short programs in short time"
    - "short lists with short programs in short time a short proof"
    - "improving the space bounded version of muchnik s conditional complexity theorem via naive derandomization"

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.CC(Computational Complexity)