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: "applications of compressed sensing in communications networks"
  Abstract: This paper presents a tutorial for CS applications in communications networks. The Shannon's sampling theorem states that to recover a signal, the sampling rate must be as least the Nyquist rate. Compressed sensing (CS) is based on the surprising fact that to recover a signal that is sparse in certain representations, one can sample at the rate far below the Nyquist rate. Since its inception in 2006, CS attracted much interest in the research community and found wide-ranging applications from astronomy, biology, communications, image and video processing, medicine, to radar. CS also found successful applications in communications networks. CS was applied in the detection and estimation of wireless signals, source coding, multi-access channels, data collection in sensor networks, and network monitoring, etc. In many cases, CS was shown to bring performance gains on the order of 10X. We believe this is just the beginning of CS applications in communications networks, and the future will see even more fruitful applications of CS in our field.

Connected through hyperedge HE-1 (induced by source paper "stronger l2 l2 compressed sensing without iterating"):
  with members:
    - "dimension independent sparse fourier transform"
    - "solving linear programs in the current matrix multiplication time"
    - "improved algorithms for adaptive compressed sensing"
    - "sublinear time algorithms for compressive phase retrieval"
    - "one bit expandersketch for one bit compressed sensing"
    - "an adaptive sublinear time block sparse fourier transform"
    - "almost optimal phaseless compressed sensing with sublinear decoding time"
    - "sample efficient estimation and recovery in sparse fft via isolation on average"

Connected through hyperedge HE-2 (induced by source paper "compressed sensing for wireless communications useful tips and tricks"):
  with members:
    - "structured compressive sensing based spatio temporal joint channel estimation for fdd massive mimo"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "compressive sensing with prior support quality information and application to massive mimo channel estimation with temporal correlation"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"
    - "spatially sparse precoding in millimeter wave mimo systems"
    - "sub nyquist sampling for power spectrum sensing in cognitive radios a unified approach"

Connected through hyperedge HE-3 (induced by source paper "distributed fronthaul compression and joint signal recovery in cloud ran"):
  with members:
    - "optimized backhaul compression for uplink cloud radio access network"
    - "linear precoding in cooperative mimo cellular networks with limited coordination clusters"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "distributed compressive sensing"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-4 (induced by source paper "separation of sinusoidal and chirp components using compressive sensing approach"):
  with members:
    - "theory and applications of compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "omp based joint sparsity pattern recovery under communication constraints"):
  with members:
    - "distributed greedy pursuit algorithms"
    - "subspace recovery from structured union of subspaces"
    - "theory and applications of compressed sensing"
    - "distributed sparse signal recovery for sensor networks"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "performance analysis for sparse support recovery"
    - "average case analysis of multichannel sparse recovery using convex relaxation"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-6 (induced by source paper "distributed compressed sensing for sensor networks with packet erasures"):
  with members:
    - "distributed compressed sensing for static and time varying networks"

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.NI(Networking and Internet Architecture)