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 channel estimation in large scale mimo using polynomial expansion"
  Abstract: This paper considers pilot-based channel estimation in large-scale multiple-input multiple-output (MIMO) communication systems, also known as "massive MIMO". Unlike previous works on this topic, which mainly considered the impact of inter-cell disturbance due to pilot reuse (so-called pilot contamination), we are concerned with the computational complexity. The conventional minimum mean square error (MMSE) and minimum variance unbiased (MVU) channel estimators rely on inverting covariance matrices, which has cubic complexity in the multiplication of number of antennas at each side. Since this is extremely expensive when there are hundreds of antennas, we propose to approximate the inversion by an L-order matrix polynomial. A set of low-complexity Bayesian channel estimators, coined Polynomial ExpAnsion CHannel (PEACH) estimators, are introduced. The coefficients of the polynomials are optimized to yield small mean square error (MSE). We show numerically that near-optimal performance is achieved with low polynomial orders. In practice, the order L can be selected to balance between complexity and MSE. Interestingly, pilot contamination is beneficial to the PEACH estimators in the sense that smaller L can be used to achieve near-optimal MSEs.

Connected through hyperedge HE-1 (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"
    - "channel estimation in massive 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"

Connected through hyperedge HE-2 (induced by source paper "low complexity polynomial channel estimation in large scale mimo with arbitrary statistics"):
  with members:
    - "linear precoding based on polynomial expansion large scale multi cell mimo systems"
    - "massive mimo for next generation wireless systems"
    - "blind pilot decontamination"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "a coordinated approach to channel estimation in large scale multiple antenna systems"
    - "asymptotic moments for interference mitigation in correlated fading channels"
    - "pilot contamination and precoding in multi cell tdd systems"

Connected through hyperedge HE-3 (induced by source paper "linear precoding based on polynomial expansion reducing complexity in massive mimo"):
  with members:
    - "linear precoding based on polynomial expansion large scale multi cell mimo systems"
    - "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "pareto characterization of the multicell mimo performance region with simple receivers"
    - "mimo interference alignment over correlated channels with imperfect csi"
    - "a new approach for capacity analysis of large dimensional multi antenna channels"

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)