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: "lumpings of markov chains entropy rate preservation and higher order lumpability"
  Abstract: A lumping of a Markov chain is a coordinate-wise projection of the chain. We characterise the entropy rate preservation of a lumping of an aperiodic and irreducible Markov chain on a finite state space by the random growth rate of the cardinality of the realisable preimage of a finite-length trajectory of the lumped chain and by the information needed to reconstruct original trajectories from their lumped images. Both are purely combinatorial criteria, depending only on the transition graph of the Markov chain and the lumping function. A lumping is strongly k-lumpable, iff the lumped process is a k-th order Markov chain for each starting distribution of the original Markov chain. We characterise strong k-lumpability via tightness of stationary entropic bounds. In the sparse setting, we give sufficient conditions on the lumping to both preserve the entropy rate and be strongly k-lumpable.

Connected through hyperedge HE-1 (induced by source paper "analytic properties of trackable weak models"):
  with members:
    - "observability properties of colored graphs"

Connected through hyperedge HE-2 (induced by source paper "co clustering via information theoretic markov aggregation"):
  with members:
    - "a generalized framework for kullback leibler markov aggregation"
    - "optimal kullback leibler aggregation via information bottleneck"
    - "optimal high level descriptions of dynamical systems"

Connected through hyperedge HE-3 (induced by source paper "higher order kullback leibler aggregation of markov chains"):
  with members:
    - "optimal kullback leibler aggregation via information bottleneck"

Connected through hyperedge HE-4 (induced by source paper "graph based lossless markov lumpings"):
  with members:
    - "information preserving markov aggregation"

Connected through hyperedge HE-5 (induced by source paper "optimal kullback leibler aggregation via information bottleneck"):
  with members:
    - "mean field variational approximation for continuous time bayesian networks"
    - "signal enhancement as minimization of relevant information loss"
    - "a metric between probability distributions on finite sets of different cardinalities and applications to order reduction"
    - "lumpability abstractions of rule based systems"

Connected through hyperedge HE-6 (induced by source paper "on the rate of information loss in memoryless systems"):
  with members:
    - "information measures for deterministic input output systems"
    - "some results on the information loss in dynamical systems"

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.IT(Information Theory)