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: "undecidability and irreducibility conditions for open ended evolution and emergence"
  Abstract: Is undecidability a requirement for open-ended evolution (OEE)? Using methods derived from algorithmic complexity theory, we propose robust computational definitions of open-ended evolution and the adaptability of computable dynamical systems. Within this framework, we show that decidability imposes absolute limits to the stable growth of complexity in computable dynamical systems. Conversely, systems that exhibit (strong) open-ended evolution must be undecidable, establishing undecidability as a requirement for such systems. Complexity is assessed in terms of three measures: sophistication, coarse sophistication and busy beaver logical depth. These three complexity measures assign low complexity values to random (incompressible) objects. As time grows, the stated complexity measures allow for the existence of complex states during the evolution of a computable dynamical system. We show, however, that finding these states involves undecidable computations. We conjecture that for similar complexity measures that assign low complexity values, decidability imposes comparable limits to the stable growth of complexity, and that such behaviour is necessary for non-trivial evolutionary systems. We show that the undecidability of adapted states imposes novel and unpredictable behaviour on the individuals or populations being modelled. Such behaviour is irreducible. Finally, we offer an example of a system, first proposed by Chaitin, that exhibits strong OEE.

Connected through hyperedge HE-1 (induced by source paper "algorithmic networks central time to trigger expected emergent open endedness"):
  with members:
    - "self referential basis of undecidable dynamics from the liar paradox and the halting problem to the edge of chaos"
    - "algorithmically probable mutations reproduce aspects of evolution such as convergence rate genetic memory and modularity"
    - "quantifying synergistic information using intermediate stochastic variables"
    - "the paradox of computability and a recursive relative version of the busy beaver function"
    - "computing halting probabilities from other halting probabilities"
    - "relativizing an incompressible number and an incompressible function through subrecursive extensions of turing machines"
    - "on multiaspect graphs"
    - "formal definitions of unbounded evolution and innovation reveal universal mechanisms for open ended evolution in dynamical systems"

Connected through hyperedge HE-2 (induced by source paper "learning the undecidable from networked systems"):
  with members:
    - "algorithmically probable mutations reproduce aspects of evolution such as convergence rate genetic memory and modularity"
    - "the paradox of computability and a recursive relative version of the busy beaver function"
    - "time centrality in dynamic complex networks"
    - "quantifying synergistic mutual information"

Connected through hyperedge HE-3 (induced by source paper "algorithmically probable mutations reproduce aspects of evolution such as convergence rate genetic memory and modularity"):
  with members:
    - "coding theorem like behaviour and emergence of the universal distribution from resource bounded algorithmic probability"
    - "low algorithmic complexity entropy deceiving graphs"
    - "a decomposition method for global evaluation of shannon entropy and local estimations of algorithmic complexity"
    - "the limits of decidable states on open ended evolution and emergence"
    - "a computable measure of algorithmic probability by finite approximations with an application to integer sequences"
    - "correlation of automorphism group size and topological properties with program size complexity evaluations of graphs and complex networks"
    - "calculating kolmogorov complexity from the output frequency distributions of small turing machines"
    - "two dimensional kolmogorov complexity and validation of the coding theorem method by compressibility"

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.OH(Other Computer Science)