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: "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.

Connected through hyperedge HE-1 (induced by source paper "multi band covariance interpolation with applications in massive mimo"):
  with members:
    - "compressive estimation of a stochastic process with unknown autocorrelation function"
    - "efficient downlink channel probing and uplink feedback in fdd massive mimo systems"
    - "truncated polynomial expansion downlink precoders and uplink detectors for massive mimo"
    - "low complexity massive mimo subspace estimation and tracking from low dimensional projections"
    - "channel vector subspace estimation from low dimensional projections"
    - "linear precoding based on polynomial expansion large scale multi cell mimo systems"
    - "mimo downlink scheduling with non perfect channel state knowledge"

Connected through hyperedge HE-2 (induced by source paper "low complexity statistically robust precoder detector computation for massive mimo systems"):
  with members:
    - "on the ergodic rate lower bounds with applications to massive mimo"
    - "low complexity massive mimo subspace estimation and tracking from low dimensional projections"
    - "uplink performance evaluation of massive mu mimo systems"
    - "fifty years of mimo detection the road to large scale mimos"
    - "linear precoding based on polynomial expansion large scale multi cell mimo systems"
    - "massive mimo for next generation wireless systems"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "achieving massive mimo spectral efficiency with a not so large number of antennas"

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)