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 "metrics for evaluating the efficiency of compressing sensing techniques"):
  Abstract: Compressive sensing has been receiving a great deal of interest from researchers in many areas because of its ability in speeding up data acquisition. This framework allows fast signal acquisition and compression when signals are sparse in some domains. It extracts the main information from high dimensional sparse signals using only a few samples, then the sparse signals are recovered from the few measurements. There are two main points to consider when it comes to using compressive sensing. The first one is how to design the linear measurement matrix to ensure that the compressive sensing is meeting the objectives of the application. The second is how to recover the sparse signal from few measurements. Performing compressive sensing requires analyzing and investigating the efficiency of the measurement matrix and the recovery algorithm. To date, constructing explicit measurement matrices and developing efficient recovery algorithms are still open challenges in applications. Thus, this paper describes metrics to evaluate the performance of compressive sensing techniques.

Includes members:
  Member "techniques for dealing with uncertainty in cognitive radio networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a cooperative spectrum sensing scheme based on compressive sensing for cognitive radio networks")
  Member "byzantine attack and defense in cognitive radio networks a survey":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "game theory for multi access edge computing survey use cases and future trends")
  Member "recovery of sparsely corrupted signals":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "boosting occluded image classification via subspace decomposition based estimation of deep features")

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)