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 "low complexity and statistically robust beamformer design for massive mimo systems"):
  Abstract: Massive MIMO is a variant of multiuser MIMO in which the number of antennas at the base station (BS) $M$ is very large and typically much larger than the number of served users (data streams) $K$. Recent research has illustrated the system-level advantages of such a system and in particular the beneficial effect of increasing the number of antennas $M$. These benefits, however, come at the cost of dramatic increase in hardware and computational complexity. This is partly due to the fact that the BS needs to compute suitable beamforming vectors in order to coherently transmit/receive data to/from each user, where the resulting complexity grows proportionally to the number of antennas $M$ and the number of served users $K$. Recently, different algorithms based on tools from random matrix theory in the asymptotic regime of $M,K \to \infty$ with $\frac{K}{M} \to \rho \in (0,1)$ have been proposed to reduce the complexity. The underlying assumption in all these techniques, however, is that the exact statistics (covariance matrix) of the channel vectors of the users is a priori known. This is far from being realistic, especially that in the high-dim regime of $M\to \infty$, estimation of the underlying covariance matrices is well known to be a very challenging problem. #R##N#In this paper, we propose a novel technique for designing beamforming vectors in a massive MIMO system. Our method is based on the randomized Kaczmarz algorithm and does not require knowledge of the statistics of the users' channel vectors. We analyze the performance of our proposed algorithm theoretically and compare its performance with that of other competitive techniques based on random matrix theory and approximate message passing via numerical simulations. Our results indicate that our proposed technique has a comparable performance while being highly robust to variations in the channel statistics.

Includes members:
  Member "optimally tuned nonparametric linear equalization for massive mu mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "decentralized equalization with feedforward architectures for massive mu mimo")
  Member "on the ergodic rate lower bounds with applications to massive mimo":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "analysis of uav communications in cell free massive mimo systems")
  Member "low complexity massive mimo subspace estimation and tracking from low dimensional projections":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an efficient beam and channel acquisition via sparsity map and joint angle delay power profile estimation for wideband massive mimo systems")
  Member "optimality of large mimo detection via approximate message passing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "fifty years of mimo detection the road to large scale mimos":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "model driven deep learning for joint mimo channel estimation and signal detection")
  Member "linear precoding based on polynomial expansion large scale multi cell mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "asymptotic analysis of rzf over double scattering channels with mmse estimation")
  Member "massive mimo for next generation wireless systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "joint power control and lsfd for wireless powered cell free massive mimo")
  Member "scaling up mimo opportunities and challenges with very large arrays":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "downlink secrecy rate of one bit massive mimo system with active eavesdropping")

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)