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: "channel estimation in massive mimo systems"
  Abstract: We introduce novel blind and semi-blind channel estimation methods for cellular time-division duplexing systems with a large number of antennas at each base station. The methods are based on the maximum a-posteriori principle given a prior for the distribution of the channel vectors and the received signals from the uplink training and data phases. Contrary to the state-of-the-art massive MIMO channel estimators which either perform linear estimation based on the pilot symbols or rely on a blind principle, the proposed semi-blind method efficiently supp resses most of the interference caused by pilot-contamination. The simulative analysis illustrate s that the semi-blind estimator outperforms stateof-the-art linear and non-linear approaches to the massive MIMO channel estimation problem. I. I NTRODUCTION Deployment of large numbers of antennas at the base stations in a cellular network can lead to huge gains in both, spectral and energy efficiency. The increase i n energy efficiency is mostly due to the array gain and the high spectral efficiency is achieved by serving s everal users simultaneously through spatial multiplexing. Under favorable propagation conditions [2], namely, i.i.d. channel coefficients for the links from the different antennas at a base station to a user terminal, the s patial channels to distinct users are close to orthogonal in such a massive MIMO system due to the law of large numbers. Consequently, the interference caused by spatial multiplexing can be suppressed with simple signal processing methods, e.g., matched filters [2]‐[4] or even constant envelope prec oding [5].

Connected through hyperedge HE-1 (induced by source paper "super resolution blind channel and signal estimation for massive mimo with one dimensional antenna array"):
  with members:
    - "blind estimation of sparse broadband massive mimo channels with ideal and one bit adcs"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "dictionary learning based sparse channel representation and estimation for fdd massive mimo systems"
    - "fundamental limits of training based multiuser mimo systems"
    - "turbo compressed sensing with partial dft sensing matrix"
    - "millimeter wave mimo with lens antenna array a new path division multiplexing paradigm"
    - "massive mimo for next generation wireless systems"

Connected through hyperedge HE-2 (induced by source paper "pilot power allocation through user grouping in multi cell massive mimo systems"):
  with members:
    - "power allocation schemes for multicell massive mimo systems"
    - "uplink pilot and data power control for single cell massive mimo systems with mrc"
    - "massive mimo with non ideal arbitrary arrays hardware scaling laws and circuit aware design"
    - "multipair full duplex relaying with massive arrays and linear processing"
    - "power scaling of uplink massive mimo systems with arbitrary rank channel means"
    - "dealing with interference in distributed large scale mimo systems a statistical approach"
    - "massive mimo multicasting in noncooperative cellular networks"
    - "massive mimo systems with non ideal hardware energy efficiency estimation and capacity limits"

Connected through hyperedge HE-3 (induced by source paper "blind estimation of sparse broadband massive mimo channels with ideal and one bit adcs"):
  with members:
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "blind signal detection in massive mimo exploiting the channel sparsity"
    - "uplink performance of wideband massive mimo with one bit adcs"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "mixed adc massive mimo"
    - "channel acquisition for massive mimo ofdm with adjustable phase shift pilots"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"
    - "one bit massive mimo channel estimation and high order modulations"

Connected through hyperedge HE-4 (induced by source paper "energy based modulation for noncoherent massive simo systems"):
  with members:
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "near maximum likelihood detector and channel estimator for uplink multiuser massive mimo systems with one bit adcs"
    - "massive mimo performance evaluation based on measured propagation data"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "massive mimo systems with non ideal hardware energy efficiency estimation and capacity limits"
    - "massive mimo for next generation wireless systems"
    - "what will 5g be"
    - "millimeter wave cellular wireless networks potentials and challenges"

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)