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 "massive mimo unsourced random access"):
  Abstract: We consider an extension of the massive unsourced random access originally proposed by Polyanskiy to the case where the receiver has a very large number of antennas (a massive MIMO base station) and no channel state information is given to the receiver (fully non-coherent detection). Our coding approach borrows the concatenated coding idea from Amalladinne et. al., combined with a novel non-Bayesian `activity detection' algorithm for massive MIMO random access channels, that outperforms currently proposed Bayesian vector AMP (VAMP) schemes currently proposed for activity detection, and does not suffer from the numerical instabilities and requirement for accurate a priori statistics as VAMP. We show that the required transmit $E_b/N_0$ for reliable communication can be made arbitrarily small as the number of receiver antennas M grows sufficiently large.

Includes members:
  Member "improved scaling law for activity detection in massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "faster activity and data detection in massive random access a multi armed bandit approach")
  Member "on the fly large scale channel gain estimation for massive antenna array base stations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "non bayesian activity detection large scale fading coefficient estimation and unsourced random access with a massive mimo receiver")
  Member "sparse activity detection for massive connectivity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "faster activity and data detection in massive random access a multi armed bandit approach")
  Member "sparse signal processing for grant free massive iot connectivity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sparcs for unsourced random access")
  Member "massive connectivity with massive mimo part i device activity detection and channel estimation":
    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 "device activity and embedded information bit detection using amp in massive mimo":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on throughput of compressive random access for one short message delivery in iot")
  Member "robust nonnegative sparse recovery and the nullspace property of 0 1 measurements":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "non bayesian activity detection large scale fading coefficient estimation and unsourced random access with a massive mimo receiver")
  Member "long range communications in unlicensed bands the rising stars in the iot and smart city scenarios":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "performance analysis of single cell adaptive data rate enabled lorawan")

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)