human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "peak reduction and clipping mitigation in ofdm by augmented compressive sensing"):
  Abstract: This work establishes the design, analysis, and fine-tuning of a peak-to-average-power-ratio (PAPR) reducing system, based on compressed sensing (CS) at the receiver of a peak-reducing sparse clipper applied to an orthogonal frequency-division multiplexing (OFDM) signal at the transmitter. By exploiting the sparsity of clipping events in the time domain relative to a predefined clipping threshold, the method depends on partially observing the frequency content of the clipping distortion over reserved tones to estimate the remaining distortion.

Includes members:
  Member "general deviants an analysis of perturbations in compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "compressive sensing for cut improvement and local clustering")
  Member "a probabilistic and ripless theory of compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "support recovery for sparse signals with unknown non stationary modulation")
  Member "necessary and sufficient conditions for sparsity pattern recovery":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "harmless interpolation of noisy data in regression")
  Member "block sparsity coherence and efficient recovery":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "machine learning for geometrically consistent angular spread function estimation in massive mimo")

Question: Please tell me which class the source paper 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)