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 "measurement matrix design for compressive sensing based mimo radar"):
  Abstract: In colocated multiple-input multiple-output (MIMO) radar using compressive sensing (CS), a receive node compresses its received signal via a linear transformation, referred to as a measurement matrix. The samples are subsequently forwarded to a fusion center, where an l1-optimization problem is formulated and solved for target information. CS-based MIMO radar exploits target sparsity in the angle-Doppler-range space and thus achieves the high localization performance of traditional MIMO radar but with significantly fewer measurements. The measurement matrix affects the recovery performance. A random Gaussian measurement matrix, typically used in CS problems, does not necessarily result in the best possible detection performance for the basis matrix corresponding to the MIMO radar scenario. This paper considers optimal measurement matrix design with the optimality criterion depending on the coherence of the sensing matrix (CSM) and/or signal-to-interference ratio (SIR). Two approaches are proposed: the first one minimizes a linear combination of CSM and the inverse SIR, and the second one imposes a structure on the measurement matrix and determines the parameters involved so that the SIR is enhanced. Depending on the transmit waveforms, the second approach can significantly improve the SIR, while maintaining a CSM comparable to that of the Gaussian random measurement matrix (GRMM). Simulations indicate that the proposed measurement matrices can improve detection accuracy as compared to a GRMM.

Includes members:
  Member "cssf mimo radar low complexity compressive sensing based mimo radar that uses step frequency":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "analysis of sparse mimo radar")
  Member "target localization accuracy gain in mimo radar based systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "suboptimum low complexity joint multi target detection and localization for noncoherent mimo radar with widely separated antennas")
  Member "mimo radar using compressive sampling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sparse blind deconvolution for distributed radar autofocus imaging")
  Member "compressive sensing for mimo radar":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "step frequency radar with compressive sampling sfr cs")
  Member "performance analysis for sparse support recovery":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sample measurement tradeoff in support recovery under a subgaussian prior")
  Member "distributed mimo radar using compressive sampling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cssf mimo radar low complexity compressive sensing based mimo radar that uses step frequency")
  Member "subspace pursuit for compressive sensing signal reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "compressive sensing for cut improvement and local clustering")
  Member "collaborative beamforming for distributed wireless ad hoc sensor networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "coherent communications in self organizing networks with distributed beamforming")

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)