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 "sparse recovery with linear and nonlinear observations dependent and noisy data"):
  Abstract: We formulate sparse support recovery as a salient set identification problem and use information-theoretic analyses to characterize the recovery performance and sample complexity. We consider a very general model where we are not restricted to linear models or specific distributions. We state non-asymptotic bounds on recovery probability and a tight mutual information formula for sample complexity. We evaluate our bounds for applications such as sparse linear regression and explicitly characterize effects of correlation or noisy features on recovery performance. We show improvements upon previous work and identify gaps between the performance of recovery algorithms and fundamental information.

Includes members:
  Member "lectures on designing screening experiments":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimal multistage group testing algorithm for 3 defectives")
  Member "sparse signal processing with linear and non linear observations a unified shannon theoretic approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "information theoretic lower bounds for compressive sensing with generative models")
  Member "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")
  Member "non adaptive group testing explicit bounds and novel algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "serial quantization for sparse time sequences")
  Member "boolean compressed sensing and noisy group testing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "serial quantization for sparse time sequences")
  Member "optimal phase transitions in compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "optimal errors and phase transitions in high dimensional generalized linear models")
  Member "the sampling rate distortion tradeoff for sparsity pattern recovery in compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "unsourced multiuser sparse regression codes achieve the symmetric mac capacity")
  Member "information theoretic bounds for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "random access for massive machine type communications")

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)