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 "on incompressible multidimensional networks"):
  Abstract: The study of complex networks has shown several applications to real-world networks. In this way, the demand for new graph abstractions in order to deal with multidimensional structures and their complexities increases. This article presents a theoretical investigation of incompressible multidimensional networks defined by generalized graph representations. In particular, we mathematically study the incompressibility (i.e., algorithmic randomness) of snapshot-dynamic networks and multiplex networks in comparison to the incompressibility of more general forms of multidimensional networks, from which snapshot-dynamic networks or multiplex networks are particular cases. In addition, from a worst-case compressibility analysis, we study some of the network topological properties of general multidimensional networks. To these ends, first we show that incompressible snapshot-dynamic (or multiplex) networks carry an amount of algorithmic information that is linearly dominated by the size of the set of time instants (or layers). This contrasts with the algorithmic information carried by incompressible general dynamic (or multilayer) networks that is of the quadratic order of the size of the set of time instants (or layers). Furthermore, we show that such incompressible general multidimensional networks have very short diameter, high k-connectivity, and degrees of the order of half of the network size within a strong-asymptotically dominated standard deviation. Then, we show that incompressible general multidimensional networks have transtemporal (crosslayer or, in general, non-sequential interdimensional) edges, i.e., edges linking vertices at non-sequential time instants (layers or, in general, elements of a node dimension).

Includes members:
  Member "emergent open endedness from contagion of the fittest":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on incompressible high order networks")
  Member "time centrality in dynamic complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "algorithmic networks central time to trigger expected emergent open endedness")

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)