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: "robust nonnegative sparse recovery and the nullspace property of 0 1 measurements"
  Abstract: We investigate recovery of nonnegative vectors from non-adaptive compressive measurements in the presence of noise of unknown power. In the absence of noise, existing results in the literature identify properties of the measurement that assure uniqueness in the non-negative orthant. By linking such uniqueness results to nullspace properties, we deduce uniform and robust compressed sensing guarantees for nonnegative least squares. No l1-regularization is required. As an important proof of principle, we establish that m x n random i.i.d. 0/1-valued Bernoulli matrices obey the required conditions with overwhelming probability provided that m=O(slog(n/s)). We achieve this by establishing the robust nullspace property for random 0/1-matrices - a novel result in its own right. Our analysis is motivated by applications in wireless network activity detection.

Connected through hyperedge HE-1 (induced by source paper "non bayesian activity detection large scale fading coefficient estimation and unsourced random access with a massive mimo receiver"):
  with members:
    - "improved scaling law for activity detection in massive mimo systems"
    - "on the fly large scale channel gain estimation for massive antenna array base stations"
    - "techniques for improving the finite length performance of sparse superposition codes"
    - "on the restricted isometry of the columnwise khatri rao product"
    - "sparse activity detection for massive connectivity"
    - "massive connectivity with massive mimo part ii achievable rate characterization"
    - "sparse signal processing for grant free massive connectivity a future paradigm for random access protocols in the internet of things"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"

Connected through hyperedge HE-2 (induced by source paper "sparcs for unsourced random access"):
  with members:
    - "massive mimo unsourced random access"
    - "techniques for improving the finite length performance of sparse superposition codes"
    - "sparse signal processing for grant free massive iot connectivity"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "on or many access channels"
    - "device activity and embedded information bit detection using amp in massive mimo"
    - "state evolution for approximate message passing with non separable functions"

Connected through hyperedge HE-3 (induced by source paper "massive access for future wireless communication systems"):
  with members:
    - "fundamental limits of many user mac with finite payloads and fading"
    - "non bayesian activity detection large scale fading coefficient estimation and unsourced random access with a massive mimo receiver"
    - "on the fundamental limits of mimo massive access communication"
    - "a joint graph based coding scheme for the unsourced random access gaussian channel"
    - "polar coding and random spreading for unsourced multiple access"
    - "energy efficient coded random access for the wireless uplink"
    - "sparcs for unsourced random access"
    - "random access channel coding in the finite blocklength regime"

Connected through hyperedge HE-4 (induced by source paper "grant free massive random access with a massive mimo receiver"):
  with members:
    - "phase transition analysis for covariance based massive random access with massive mimo"
    - "non bayesian activity detection large scale fading coefficient estimation and unsourced random access with a massive mimo receiver"
    - "on the restricted isometry property of centered self khatri rao products"
    - "polar coding and random spreading for unsourced multiple access"
    - "an enhanced decoding algorithm for coded compressed sensing"
    - "improved scaling law for activity detection in massive mimo systems"
    - "on the fly large scale channel gain estimation for massive antenna array base stations"
    - "on the restricted isometry of the columnwise khatri rao product"

Connected through hyperedge HE-5 (induced by source paper "machine learning for geometrically consistent angular spread function estimation in massive mimo"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "deep learning methods for improved decoding of linear codes"
    - "fdd massive mimo via ul dl channel covariance extrapolation and active channel sparsification"
    - "deep learning for joint source channel coding of text"
    - "massive mimo pilot decontamination and channel interpolation via wideband sparse channel estimation"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "on deep learning based channel decoding"

Connected through hyperedge HE-6 (induced by source paper "massive mimo unsourced random access"):
  with members:
    - "improved scaling law for activity detection in massive mimo systems"
    - "on the fly large scale channel gain estimation for massive antenna array base stations"
    - "sparse activity detection for massive connectivity"
    - "sparse signal processing for grant free massive iot connectivity"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "device activity and embedded information bit detection using amp in massive mimo"
    - "long range communications in unlicensed bands the rising stars in the iot and smart city scenarios"
    - "belief propagation for joint sparse recovery"

Connected through hyperedge HE-7 (induced by source paper "sparse non negative recovery from biased subgaussian measurements using nnls"):
  with members:
    - "on the fly large scale channel gain estimation for massive antenna array base stations"
    - "visible light communications based indoor positioning via compressed sensing"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "learning without concentration"
    - "exact and stable covariance estimation from quadratic sampling via convex programming"
    - "compressed sensing performance bounds under poisson noise"

Connected through hyperedge HE-8 (induced by source paper "efficient beam alignment for mmwave single carrier systems with hybrid mimo transceivers"):
  with members:
    - "single carrier modulation versus ofdm for millimeter wave wireless mimo"
    - "throughput optimal beam alignment in millimeter wave networks"
    - "frequency domain compressive channel estimation for frequency selective hybrid mmwave mimo systems"
    - "on the ergodic rate lower bounds with applications to massive mimo"
    - "millimeter wave mimo channel estimation using overlapped beam patterns and rate adaptation"
    - "a robust time domain beam alignment scheme for multi user wideband mmwave systems"
    - "channel estimation for hybrid architecture based wideband millimeter wave systems"
    - "tracking mm wave channel dynamics fast beam training strategies under mobility"

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)