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: "information loss in static nonlinearities"
  Abstract: In this work, conditional entropy is used to quantify the information loss induced by passing a continuous random variable through a memoryless nonlinear input-output system. We derive an expression for the information loss depending on the input density and the nonlinearity and show that the result is strongly related to the non-injectivity of the considered system. Tight upper bounds are presented, which can be evaluated with less difficulty than a direct evaluation of the information loss, which involves the logarithm of a sum. Application of our results is illustrated on a set of examples.

Connected through hyperedge HE-1 (induced by source paper "information measures for deterministic input output systems"):
  with members:
    - "relative information loss in the pca"
    - "signal enhancement as minimization of relevant information loss"
    - "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-2 (induced by source paper "relative information loss in the pca"):
  with members:
    - "relative information loss an introduction"
    - "signal enhancement as minimization of relevant information loss"
    - "on the information loss in memoryless systems the multivariate case"
    - "entropy of the mixture of sources and entropy dimension"

Connected through hyperedge HE-3 (induced by source paper "signal enhancement as minimization of relevant information loss"):
  with members:
    - "relative information loss in the pca"
    - "relative information loss an introduction"
    - "on the information loss in memoryless systems the multivariate case"

Connected through hyperedge HE-4 (induced by source paper "on the information loss in memoryless systems the multivariate case"):
  with members:
    - "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)