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 "performance limits of segmented compressive sampling correlated measurements versus bits"):
  Abstract: This paper discusses performance limits of the segmented compressive sampling (CS) system that collects correlated measurements. It is shown that the effect of correlation among measurements for the segmented CS can be characterized by a penalty term in the corresponding bounds on the measurement rate. Moreover, this penalty term is vanishing as the signal dimension increases. It means that the performance degradation due to the fixed correlation among measurements obtained by the segmented CS (as compared to the standard CS with equivalent size measurement matrix) is negligible for a high-dimensional signal. In combination with the fact that the signal reconstruction quality improves with additional measurements obtained by the segmented CS (as compared to the standard CS without additional measurements), the fact that the additional correlated measurements also provide new information about a signal is a strong argument for the segmented CS.

Includes members:
  Member "segmented compressed sampling for analog to information conversion method and performance analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gridless quadrature compressive sampling with interpolated array technique")
  Member "approximate sparsity pattern recovery information theoretic lower bounds":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "understanding phase transitions via mutual information and mmse")
  Member "information theoretic bounds for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "random access for massive machine type communications")
  Member "shannon theoretic limits on noisy compressive sampling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "information theoretic lower bounds for compressive sensing with generative models")

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)