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 "phase transition analysis for covariance based massive random access with massive mimo"):
  Abstract: This paper considers the massive random access problem in which a large number of sporadically active devices wish to communicate with a base-station (BS) equipped with a large number of antennas. Each device is preassigned a unique signature sequence, and the BS identifies the active devices in the random access by detecting which sequences are transmitted. This device activity detection problem can be formulated as a maximum likelihood estimation (MLE) problem with the sample covariance matrix of the received signal being a sufficient statistic. The aim of this paper is to characterize the parameter space in which this covariance based approach would be able to successfully recover the device activities in the massive multiple-input multiple-output (MIMO) regime. Through an analysis of the asymptotic behaviors of MLE via its associated Fisher information matrix, this paper derives a necessary and sufficient condition on the Fisher information matrix to ensure a vanishing detection error rate as the number of antennas goes to infinity, based on which a numerical phase transition analysis is obtained. This condition is also examined from a perspective of covariance matching that relates the phase transition analysis in this paper to a recently derived scaling law. Furthermore, this paper provides a characterization of the distribution of the estimation error in MLE, based on which the error probabilities in device activity detection can be accurately predicted. Finally, this paper studies a random access scheme with joint device activity and data detection and analyzes its performance in a similar way. Simulation results validate the analysis.

Includes members:
  Member "a dimension reduction based joint activity detection and channel estimation algorithm for massive access":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "joint activity detection and channel estimation for mmw thz wideband massive access")
  Member "non bayesian activity detection large scale fading coefficient estimation and unsourced random access with a massive mimo receiver":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unsourced multiuser sparse regression codes achieve the symmetric mac capacity")
  Member "massive mimo unsourced random access":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "massive unsourced random access based on uncoupled compressive sensing another blessing of massive mimo")
  Member "joint active user detection and channel estimation in massive access systems exploiting reed muller sequences":
    also connected through other hyperedges:
      (none)
  Member "multi cell sparse activity detection for massive random access massive mimo versus cooperative mimo":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a dimension reduction based joint activity detection and channel estimation algorithm for massive access")
  Member "improved scaling law for activity detection in massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "faster activity and data detection in massive random access a multi armed bandit approach")
  Member "grant free massive mtc enabled massive mimo a compressive sensing approach":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "faster activity and data detection in massive random access a multi armed bandit approach")
  Member "sparse activity detection for massive connectivity":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "faster activity and data detection in massive random access a multi armed bandit approach")

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)