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: "a metric between probability distributions on finite sets of different cardinalities and applications to order reduction"
  Abstract: With increasing use of digital control it is natural to view control inputs and outputs as stochastic processes assuming values over finite alphabets rather than in a Euclidean space. As control over networks becomes increasingly common, data compression by reducing the size of the input and output alphabets without losing the fidelity of representation becomes relevant. This requires us to define a notion of distance between two stochastic processes assuming values in distinct sets, possibly of different cardinalities. If the two processes are i.i.d., then the problem becomes one of defining a metric between two probability distributions over distinct finite sets of possibly different cardinalities. This is the problem addressed in the present paper. A metric is defined in terms of a joint distribution on the product of the two sets, which has the two given distributions as its marginals, and has minimum entropy. Computing the metric exactly turns out to be NP-hard. Therefore an efficient greedy algorithm is presented for finding an upper bound on the distance. This problem also turns out to be NP-hard, so again a greedy algorithm is constructed for finding a suboptimal reduced order approximation. Taken together, all the results presented here permit the approximation of an i.i.d. process over a set of large cardinality by another i.i.d. process over a set of smaller cardinality. In future work, attempts will be made to extend this work to Markov processes over finite sets.

Connected through hyperedge HE-1 (induced by source paper "minimum entropy couplings and their applications"):
  with members:
    - "strong functional representation lemma and applications to coding theorems"
    - "bounds on the entropy of a function of a random variable and their applications"
    - "mutual information bounds via adjacency events"
    - "entropic causal inference"
    - "on the entropy of couplings"
    - "sumset and inverse sumset inequalities for differential entropy and mutual information"
    - "entropic inequalities and marginal problems"
    - "entropy bounds for discrete random variables via maximal coupling"

Connected through hyperedge HE-2 (induced by source paper "reduction of markov chains using a value of information based approach"):
  with members:
    - "analysis of agent expertise in ms pac man using value of information based policies"
    - "guided policy exploration for markov decision processes using an uncertainty based value of information criterion"
    - "an analysis of the value of information when exploring stochastic discrete multi armed bandits"
    - "optimal kullback leibler aggregation via information bottleneck"

Connected through hyperedge HE-3 (induced by source paper "how to find a joint probability distribution of minimum entropy almost given the marginals"):
  with members:
    - "entropic causal inference"
    - "on the entropy of couplings"

Connected through hyperedge HE-4 (induced by source paper "on the entropy of couplings"):
  with members:
    - "some properties of renyi entropy over countably infinite alphabets"
    - "entropy bounds for discrete random variables via maximal coupling"
    - "on the hardness of entropy minimization and related problems"
    - "minimum entropy combinatorial optimization problems"
    - "finding the maximizers of the information divergence from an exponential family"

Connected through hyperedge HE-5 (induced by source paper "optimal kullback leibler aggregation via information bottleneck"):
  with members:
    - "lumpings of markov chains entropy rate preservation and higher order lumpability"
    - "mean field variational approximation for continuous time bayesian networks"
    - "signal enhancement as minimization of relevant information loss"
    - "lumpability abstractions of rule based 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.SY(Systems and Control)