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 "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].

Includes members:
  Member "uplink interference reduction in large scale antenna systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "performance of multi cell massive mimo systems with interference decoding")
  Member "dealing with interference in distributed large scale mimo systems a statistical approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "covariance aided csi acquisition with non orthogonal pilots in massive mimo a large system performance analysis")
  Member "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dl based csi feedback and cooperative recovery in massive mimo")
  Member "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "energy efficiency optimization for downlink massive mimo with statistical csit")
  Member "hierarchical interference mitigation for massive mimo cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "joint path selection and rate allocation framework for 5g self backhauled mm wave networks")
  Member "blind pilot decontamination":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the pilot contamination attack in multi cell multiuser massive mimo networks")
  Member "scaling up mimo opportunities and challenges with very large arrays":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "downlink secrecy rate of one bit massive mimo system with active eavesdropping")
  Member "a coordinated approach to channel estimation in large scale multiple antenna systems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the pilot contamination attack in multi cell multiuser massive mimo networks")

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)