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 "symmetry and algorithmic complexity of polyominoes and polyhedral graphs"):
  Abstract: We introduce a definition of algorithmic symmetry able to capture essential aspects of geometric symmetry. We review, study and apply a method for approximating the algorithmic complexity (also known as Kolmogorov-Chaitin complexity) of graphs and networks based on the concept of Algorithmic Probability (AP). AP is a concept (and method) capable of recursively enumeration all properties of computable (causal) nature beyond statistical regularities. We explore the connections of algorithmic complexity---both theoretical and numerical---with geometric properties mainly symmetry and topology from an (algorithmic) information-theoretic perspective. We show that approximations to algorithmic complexity by lossless compression and an Algorithmic Probability-based method can characterize properties of polyominoes, polytopes, regular and quasi-regular polyhedra as well as polyhedral networks, thereby demonstrating its profiling capabilities.

Includes members:
  Member "low algorithmic complexity entropy deceiving graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the thermodynamics of network coding and an algorithmic refinement of the principle of maximum entropy")
  Member "a decomposition method for global evaluation of shannon entropy and local estimations of algorithmic complexity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "algorithmic probability guided supervised machine learning on non differentiable spaces")
  Member "correlation of automorphism group size and topological properties with program size complexity evaluations of graphs and complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the thermodynamics of network coding and an algorithmic refinement of the principle of maximum entropy")
  Member "calculating kolmogorov complexity from the output frequency distributions of small turing machines":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "how incomputable is kolmogorov complexity")
  Member "structure induction by lossless graph compression":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "survey and taxonomy of lossless graph compression and space efficient graph representations")

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)