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: "a fast noniterative algorithm for compressive sensing using binary measurement matrices"
  Abstract: In this paper we present a new algorithm for compressive sensing that makes use of binary measurement matrices and achieves exact recovery of sparse vectors, in a single pass, without any iterations. Our algorithm is hundreds of times faster than $\ell_1$-norm minimization, and methods based on expander graphs (which require multiple iterations). Moreover, our method requires the fewest measurements amongst all methods that use binary measurement matrices. The algorithm can accommodate nearly sparse vectors, in which case it recovers the largest components, and can also #R##N#Numerical experiments with randomly generated sparse vectors indicate that the sufficient conditions for our algorithm to work are very close to being necessary. In contrast, the best known sufficient condition for $\ell_1$-norm minimization to recover a sparse vector, namely the Restricted Isometry Property (RIP), is about thirty times away from being necessary. Therefore it would be worthwhile to explore alternate and improved sufficient conditions for $\ell_1$-norm minimization to achieve the recovery of sparse vectors.

Connected through hyperedge HE-1 (induced by source paper "sparse graph codes for non adaptive quantitative group testing"):
  with members:
    - "a simple and efficient strategy for the coin weighing problem with a spring scale"
    - "group testing using left and right regular sparse graph codes"
    - "saffron a fast efficient and robust framework for group testing based on sparse graph codes"
    - "sub linear time support recovery for compressed sensing using sparse graph codes"
    - "optimal nested test plan for combinatorial quantitative group testing"
    - "compressed sensing with sparse binary matrices instance optimal error guarantees in near optimal time"

Connected through hyperedge HE-2 (induced by source paper "compressed csi feedback with learned measurement matrix for mmwave massive mimo"):
  with members:
    - "compressive sensing techniques for next generation wireless communications"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "compressed sensing for wireless communications useful tips and tricks"
    - "compressed sensing based multi user millimeter wave systems how many measurements are needed"

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)