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: "spatial compressive sensing for mimo radar"
  Abstract: We study compressive sensing in the spatial domain to achieve target localization, specifically direction of arrival (DOA), using multiple-input multiple-output (MIMO) radar. A sparse localization framework is proposed for a MIMO array in which transmit and receive elements are placed at random. This allows for a dramatic reduction in the number of elements needed, while still attaining performance comparable to that of a filled (Nyquist) array. By leveraging properties of structured random matrices, we develop a bound on the coherence of the resulting measurement matrix, and obtain conditions under which the measurement matrix satisfies the so-called isotropy property. The coherence and isotropy concepts are used to establish uniform and non-uniform recovery guarantees within the proposed spatial compressive sensing framework. In particular, we show that non-uniform recovery is guaranteed if the product of the number of transmit and receive elements, MN (which is also the number of degrees of freedom), scales with K(logG)2, where K is the number of targets and G is proportional to the array aperture and determines the angle resolution. In contrast with a filled virtual MIMO array where the product MN scales linearly with G, the logarithmic dependence on G in the proposed framework supports the high-resolution provided by the virtual array aperture while using a small number of MIMO radar elements. In the numerical results we show that, in the proposed framework, compressive sensing recovery algorithms are capable of better performance than classical methods, such as beamforming and MUSIC.

Connected through hyperedge HE-1 (induced by source paper "serial quantization for sparse time sequences"):
  with members:
    - "learning distributions from their samples under communication constraints"
    - "information theoretic and algorithmic thresholds for group testing"
    - "efficient data collection over multiple access wireless sensors network"
    - "efficient nonrandom construction and decoding of non adaptive group testing"
    - "quantization of compressive samples with stable and robust recovery"
    - "quantization and compressive sensing"
    - "distributed quantization for measurement of correlated sparse sources over noisy channels"
    - "distributed compressed sensing for static and time varying networks"

Connected through hyperedge HE-2 (induced by source paper "serial quantization for representing sparse signals"):
  with members:
    - "learning distributions from their samples under communication constraints"
    - "hardware limited task based quantization"
    - "efficient data collection over multiple access wireless sensors network"
    - "quantization of compressive samples with stable and robust recovery"
    - "quantization and compressive sensing"
    - "group testing algorithms bounds and simulations"
    - "quantized iterative hard thresholding bridging 1 bit and high resolution quantized compressed sensing"
    - "gespar efficient phase retrieval of sparse signals"

Connected through hyperedge HE-3 (induced by source paper "action assembly sparse imitation learning for text based games with combinatorial action spaces"):
  with members:
    - "using reinforcement learning to learn how to play text based games"
    - "textworld a learning environment for text based games"
    - "towards solving text based games by producing adaptive action spaces"
    - "counting to explore and generalize in text based games"
    - "an actor critic algorithm for sequence prediction"
    - "deep reinforcement learning in large discrete action spaces"
    - "deep reinforcement learning with a natural language action space"
    - "language understanding for text based games using deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "analysis of frequency agile radar via compressed sensing"):
  with members:
    - "sparse doppler sensing based on nested arrays"
    - "spectrum sharing radar coexistence via xampling"
    - "refined analysis of sparse mimo radar"
    - "fundamental limits of hrr profiling and velocity compensation for stepped frequency waveforms"
    - "structured random measurements in signal processing"
    - "quadrature compressive sampling for radar signals"
    - "sub nyquist radar via doppler focusing"
    - "extended range profiling in stepped frequency radar with sparse recovery"

Connected through hyperedge HE-5 (induced by source paper "hidden vehicles positioning via asynchronous v2v transmission a multi path geometry approach"):
  with members:
    - "millimeter wave v2v communications distributed association and beam alignment"
    - "task replication for deadline constrained vehicular cloud computing optimal policy performance analysis and implications on road traffic"
    - "inverse multipath fingerprinting for millimeter wave v2i beam alignment"
    - "millimeter wave vehicular communication to support massive automotive sensing"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "the impact of beamwidth on temporal channel variation in vehicular channels and its implications"
    - "communication using a large scale array of ubiquitous antennas a geometry approach"

Connected through hyperedge HE-6 (induced by source paper "on the sample complexity of multichannel frequency estimation via convex optimization"):
  with members:
    - "sparse methods for direction of arrival estimation"
    - "atomic norm minimization for modal analysis from random and compressed samples"
    - "compressive spectral estimation with single snapshot esprit stability and resolution"
    - "the restricted isometry property of subsampled fourier matrices"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "exact joint sparse frequency recovery via optimization methods"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "enhancing sparsity and resolution via reweighted atomic norm minimization"

Connected through hyperedge HE-7 (induced by source paper "optimum co design for spectrum sharing between matrix completion based mimo radars and a mimo communication system"):
  with members:
    - "mimo mc radar a mimo radar approach based on matrix completion"
    - "overlapped mimo radar waveform design for coexistence with communication systems"
    - "matrix completion in colocated mimo radar recoverability bounds theoretical guarantees"
    - "target estimation in colocated mimo radar via matrix completion"
    - "complex valued random vectors and channels entropy divergence and capacity"
    - "mimo radar using compressive sampling"
    - "dynamic resource allocation in cognitive radio networks"
    - "matrix completion with noise"

Connected through hyperedge HE-8 (induced by source paper "summer sub nyquist mimo radar"):
  with members:
    - "cognitive sub nyquist hardware prototype of a collocated mimo radar"
    - "mimo mc radar a mimo radar approach based on matrix completion"
    - "recovery of sparse matrices via matrix sketching"
    - "matrix completion in colocated mimo radar recoverability bounds theoretical guarantees"
    - "fourier domain beamforming the path to compressed ultrasound imaging"
    - "analysis of sparse mimo radar"
    - "compressed beamforming in ultrasound imaging"
    - "sub nyquist radar via doppler focusing"

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)