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: "on the concentration of the crest factor for ofdm signals"
  Abstract: This paper applies several concentration inequalities to prove concentration results for the crest factor of OFDM signals. The considered approaches are, to the best of our knowledge, new in the context of establishing concentration for OFDM signals.

Connected through hyperedge HE-1 (induced by source paper "multicarrier papr reduction by iteratively shifting and concentrating the probability measure"):
  with members:
    - "5gnow challenging the lte design paradigms of orthogonality and synchronicity"
    - "the papr problem in ofdm transmission new directions for a long lasting problem"

Connected through hyperedge HE-2 (induced by source paper "the papr problem in ofdm transmission new directions for a long lasting problem"):
  with members:
    - "peak reduction and clipping mitigation in ofdm by augmented compressive sensing"
    - "expected supremum of a random linear combination of shifted kernels"
    - "on the peak to average power ratio reduction problem for orthogonal transmission schemes"
    - "analysis of alternative metrics for the papr problem in ofdm transmission"
    - "clipping noise cancellation for ofdm and ofdma systems using compressed sensing"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "on cosets of the generalized first order reed muller code with low pmepr"

Connected through hyperedge HE-3 (induced by source paper "moderate deviations analysis of binary hypothesis testing"):
  with members:
    - "on refined versions of the azuma hoeffding inequality with applications in information theory"
    - "moderate deviations of lossy source coding for discrete and gaussian sources"

Connected through hyperedge HE-4 (induced by source paper "on refined versions of the azuma hoeffding inequality with applications in information theory"):
  with members:
    - "moderate deviations analysis of binary hypothesis testing"
    - "moderate deviations of lossy source coding for discrete and gaussian sources"
    - "on universal properties of capacity approaching ldpc code ensembles"
    - "information spectrum approach to second order coding rate in channel coding"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"
    - "tight bounds on the capacity of binary input random cdma systems"
    - "on the rate of channel polarization"
    - "tight bounds for ldpc and ldgm codes under map decoding"

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)