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: "signal enhancement as minimization of relevant information loss"
  Abstract: We introduce the notion of relevant information loss for the purpose of casting the signal enhancement problem in information-theoretic terms. We show that many algorithms from machine learning can be reformulated using relevant information loss, which allows their application to the aforementioned problem. As a particular example we analyze principle component analysis for dimensionality reduction, discuss its optimality, and show that the relevant information loss can indeed vanish if the relevant information is concentrated on a lower-dimensional subspace of the input space.

Connected through hyperedge HE-1 (induced by source paper "information loss in the human auditory system"):
  with members:
    - "estimating mixture entropy with pairwise distances"

Connected through hyperedge HE-2 (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"
    - "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-3 (induced by source paper "information measures for deterministic input output systems"):
  with members:
    - "relative information loss in the pca"
    - "information loss in static nonlinearities"
    - "on the information loss in memoryless systems the multivariate case"
    - "optimal phase transitions in compressed sensing"
    - "a characterization of entropy in terms of information loss"
    - "some results on the information loss in dynamical systems"

Connected through hyperedge HE-4 (induced by source paper "information loss and anti aliasing filters in multirate systems"):
  with members:
    - "on the rate of information loss in memoryless systems"
    - "shannon meets nyquist capacity of sampled gaussian channels"
    - "relative information loss in the pca"

Connected through hyperedge HE-5 (induced by source paper "relative information loss in the pca"):
  with members:
    - "relative information loss an introduction"
    - "information loss in static nonlinearities"
    - "on the information loss in memoryless systems the multivariate case"
    - "entropy of the mixture of sources and entropy dimension"

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)