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 "recovery of signals with low density"):
  Abstract: Sparse signals (i.e., vectors with a small number of non-zero entries) build the foundation of most kernel (or nullspace) results, uncertainty relations, and recovery guarantees in the sparse signal processing and compressive sensing literature. In this paper, we introduce a novel signal-density measure that extends the common notion of sparsity to non-sparse signals whose entries' magnitudes decay rapidly. By taking into account such magnitude information, we derive a more general and less restrictive kernel result and uncertainty relation. Furthermore, we demonstrate the effectiveness of the proposed signal-density measure by showing that orthogonal matching pursuit (OMP) provably recovers low-density signals with up to 2$\times$ more non-zero coefficients compared to that guaranteed by standard results for sparse signals.

Includes members:
  Member "relaxed recovery conditions for omp ols by exploiting both coherence and decay":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sparse regression codes")
  Member "uncertainty relations and sparse signal recovery for pairs of general signal sets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sparse representation in fourier and local bases using prosparse a probabilistic analysis")
  Member "on the theorem of uniform recovery of random sampling matrices":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "matrix infinitely divisible series tail inequalities and applications in optimization")
  Member "performance analysis of sparse recovery based on constrained minimal singular values":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sparse recovery based on q ratio constrained minimal singular values")
  Member "recovery of sparsely corrupted signals":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "metrics for evaluating the efficiency of compressing sensing techniques")

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)