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: "the generalized asymptotic equipartition property necessary and sufficient conditions"
  Abstract: Suppose a string X1 n = (X1, X2,...,Xn) generated by a memoryless source (Xn)n ges 1 with distribution P is to be compressed with distortion no greater than D ges 0, using a memoryless random codebook with distribution Q. The compression performance is determined by the "generalized asymptotic equipartition property" (AEP), which states that the probability of finding a D-close match between X1 n and any given codeword Y1 n, is approximately 2-nR(P, Q, D), where the rate function R(P, Q, D) can be expressed as an infimum of relative entropies. The main purpose here is to remove various restrictive assumptions on the validity of this result that have appeared in the recent literature. Necessary and sufficient conditions for the generalized AEP are provided in the general setting of abstract alphabets and unbounded distortion measures. All possible distortion levels D ges 0 are considered; the source (Xn)n ges 1 can be stationary and ergodic; and the codebook distribution can have memory. Moreover, the behavior of the matching probability is precisely characterized, even when the generalized AEP is not valid. Natural characterizations of the rate function R(P, Q, D) are established under equally general conditions.

Connected through hyperedge HE-1 (induced by source paper "the rate distortion function of asynchronously sampled memoryless cyclostationary gaussian processes"):
  with members:
    - "the capacity of memoryless channels with sampled cyclostationary gaussian noise"
    - "on the capacity of narrowband plc channels"
    - "the distortion rate function of cyclostationary gaussian processes"
    - "frequency shift filtering for ofdm signal recovery in narrowband power line communications"
    - "on the optimal compressions in the compress and forward relay schemes"
    - "achieving the gaussian rate distortion function by prediction"

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)