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: "rsp based analysis for sparsest and least ell_1 norm solutions to underdetermined linear systems"
  Abstract: Recently, the worse-case analysis, probabilistic analysis and empirical justification have been employed to address the fundamental question: When does l1-minimization find the sparsest solution to an underdetermined linear system? In this paper, a deterministic analysis, rooted in the classic linear programming theory, is carried out to further address this question. We first identify a necessary and sufficient condition for the uniqueness of least l1-norm solutions to linear systems. From this condition, we deduce that a sparsest solution coincides with the unique least l1-norm solution to a linear system if and only if the so-called range space property (RSP) holds at this solution. This yields a broad understanding of the relationship between l0- and l1-minimization problems. Our analysis indicates that the RSP truly lies at the heart of the relationship between these two problems. Through RSP-based analysis, several important questions in this field can be largely addressed. For instance, how to efficiently interpret the gap between the current theory and the actual numerical performance of l1-minimization by a deterministic analysis, and if a linear system has multiple sparsest solutions, when does l1-minimization guarantee to find one of them? Moreover, new matrix properties (such as the RSP of order K and the Weak-RSP of order K) are introduced in this paper, and a new theory for sparse signal recovery based on the RSP of order K is established.

Connected through hyperedge HE-1 (induced by source paper "1 bit compressive sensing reformulation and rrsp based sign recovery theory"):
  with members:
    - "blind one bit compressive sampling"
    - "robust 1 bit compressive sensing via gradient support pursuit"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "one bit compressed sensing with non gaussian measurements"
    - "regime change bit depth versus measurement rate in compressive sensing"
    - "one bit compressed sensing by linear programming"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "on polar polytopes and the recovery of sparse representations"

Connected through hyperedge HE-2 (induced by source paper "uniqueness conditions for a class of l0 minimization problems"):
  with members:
    - "on partial sparse recovery"
    - "modified cs modifying compressive sensing for problems with partially known support"

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)