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: "relative information loss in the pca"
  Abstract: In this work we analyze principle component analysis (PCA) as a deterministic input-output system. We show that the relative information loss induced by reducing the dimensionality of the data after performing the PCA is the same as in dimensionality reduction without PCA. Furthermore, we analyze the case where the PCA uses the sample covariance matrix to compute the rotation. If the rotation matrix is not available at the output, we show that an infinite amount of information is lost. The relative information loss is shown to decrease with increasing sample size.

Connected through hyperedge HE-1 (induced by source paper "information measures for deterministic input output systems"):
  with members:
    - "signal enhancement as minimization of relevant information loss"
    - "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-2 (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"
    - "signal enhancement as minimization of relevant information loss"

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

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

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)