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: "reliable recovery of hierarchically sparse signals and application in machine type communications"
  Abstract: We examine and propose a solution to the problem of recovering a block sparse signal with sparse blocks from linear measurements. Such problems naturally emerge in the context of mobile communication, in settings motivated by desiderata of a 5G framework. We introduce a new variant of the Hard Thresholding Pursuit (HTP) algorithm referred to as HiHTP. For the specific class of sparsity structures, HiHTP performs significantly better in numerical experiments compared to HTP. We provide both a proof of convergence and a recovery guarantee for noisy Gaussian measurements that exhibit an improved asymptotic scaling in terms of the sampling complexity in comparison with the usual HTP algorithm.

Connected through hyperedge HE-1 (induced by source paper "simultaneous structures in convex signal recovery revisiting the convex combination of norms"):
  with members:
    - "sparse power factorization balancing peakiness and sample complexity"
    - "near optimal sample complexity for convex tensor completion"
    - "branchhull convex bilinear inversion from the entrywise product of signals with known signs"
    - "low rank tensor recovery via iterative hard thresholding"
    - "tensor theta norms and low rank recovery"
    - "self calibration and biconvex compressive sensing"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "sparse signal processing concepts for efficient 5g system design"

Connected through hyperedge HE-2 (induced by source paper "towards massive connectivity support for scalable mmtc communications in 5g networks"):
  with members:
    - "a random access protocol for pilot allocation in crowded massive mimo systems"
    - "compressive coded random access for massive mtc traffic in 5g systems"
    - "reliable and efficient access for alarm initiated and regular m2m traffic in ieee 802 11ah systems"
    - "massive machine type communication mmtc access with integrated authentication"
    - "pulse shaped ofdm for 5g systems"
    - "coded pilot access a random access solution for massive mimo systems"
    - "random access for massive mimo systems with intra cell pilot contamination"
    - "compressive random access using a common overloaded control channel"

Connected through hyperedge HE-3 (induced by source paper "hierarchical restricted isometry property for kronecker product measurements"):
  with members:
    - "low overhead hierarchically sparse channel estimation for multiuser wideband massive mimo"
    - "secure massive iot using hierarchical fast blind deconvolution"
    - "performance of hierarchical sparse detectors for massive mtc"
    - "hierarchical sparse channel estimation for massive mimo"
    - "channel acquisition for massive mimo ofdm with adjustable phase shift pilots"
    - "blind deconvolution meets blind demixing algorithms and performance bounds"
    - "compressive random access using a common overloaded control channel"
    - "sparse signal processing concepts for efficient 5g system design"

Connected through hyperedge HE-4 (induced by source paper "secure massive iot using hierarchical fast blind deconvolution"):
  with members:
    - "regularized gradient descent a nonconvex recipe for fast joint blind deconvolution and demixing"
    - "compressive coded random access for massive mtc traffic in 5g systems"
    - "blind demixing and deconvolution at near optimal rate"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "blind deconvolution meets blind demixing algorithms and performance bounds"
    - "compressive random access using a common overloaded control channel"
    - "sparse signal processing concepts for efficient 5g system design"
    - "simultaneously structured models with application to sparse and low rank matrices"

Connected through hyperedge HE-5 (induced by source paper "performance of hierarchical sparse detectors for massive mtc"):
  with members:
    - "massive connectivity with massive mimo part ii achievable rate characterization"
    - "random pilot and data access in massive mimo for machine type communications"
    - "massive machine type communications in 5g physical and mac layer solutions"
    - "compressive random access using a common overloaded control channel"
    - "characterization of coded random access with compressive sensing based multi user detection"
    - "asynchronous code division random access using convex optimization"
    - "model based compressive sensing"

Connected through hyperedge HE-6 (induced by source paper "hierarchical sparse channel estimation for massive mimo"):
  with members:
    - "massive mimo pilot decontamination and channel interpolation via wideband sparse channel estimation"
    - "channel acquisition for massive mimo ofdm with adjustable phase shift pilots"
    - "compressive random access using a common overloaded control channel"
    - "direction of arrival estimation using co prime arrays a super resolution viewpoint"
    - "towards a mathematical theory of super resolution"
    - "model based compressive sensing"

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)