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: "fundamental limits of training based multiuser mimo systems"
  Abstract: In this paper, we endeavour to seek a fundamental understanding of the potentials and limitations of training-based multiuser multiple-input multiple-output (MIMO) systems. In a multiuser MIMO system, users are geographically separated. So, the near-far effect plays an indispensable role in channel fading. The existing optimal training design for conventional MIMO does not take the near-far effect into account, and thus is not applicable to a multiuser MIMO system. In this work, we use the majorization theory as a basic tool to study the tradeoff between the channel estimation quality and the information throughput. We establish tight upper and lower bounds of the throughput, and prove that the derived lower bound is asymptotically optimal for throughput maximization at high signal-to-noise ratio. Our analysis shows that the optimal training sequences for throughput maximization in a multiuser MIMO system are in general not orthogonal to each other. Furthermore, due to the near-far effect, the optimal training design for throughput maximization is to deactivate a portion of users with the weakest channels in transmission. These observations shed light on the practical design of training-based multiuser MIMO systems.

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"
    - "turbo compressed sensing with partial dft sensing matrix"
    - "channel estimation in massive mimo systems"
    - "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 "structured turbo compressed sensing for downlink massive mimo ofdm channel estimation"):
  with members:
    - "denoising based turbo compressed sensing"
    - "approximate message passing with nearest neighbor sparsity pattern learning"
    - "structured compressive sensing based spatio temporal joint channel estimation for fdd massive mimo"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"
    - "turbo compressed sensing with partial dft sensing matrix"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "compressive sensing with prior support quality information and application to massive mimo channel estimation with temporal correlation"
    - "on the performance of turbo signal recovery with partial dft sensing matrices"

Connected through hyperedge HE-3 (induced by source paper "multi cell multiuser massive mimo networks user capacity analysis and pilot design"):
  with members:
    - "user load analysis and pilot sequence design for multi cell massive mimo networks"
    - "performance analysis and location optimization for massive mimo systems with circularly distributed antennas"
    - "graph coloring based pilot allocation to mitigate pilot contamination for multi cell massive mimo systems"
    - "power allocation schemes for multicell massive mimo systems"
    - "pilot signal design for massive mimo systems a received signal to noise ratio based approach"
    - "massive mimo performance evaluation based on measured propagation data"
    - "smart pilot assignment for massive mimo"
    - "massive mimo for next generation wireless systems"

Connected through hyperedge HE-4 (induced by source paper "locally orthogonal training design for cloud rans based on graph coloring"):
  with members:
    - "rrh based massive mimo with on the fly pilot contamination control"
    - "cell free massive mimo versus small cells"
    - "selective uplink training for massive mimo systems"
    - "superimposed pilots are superior for mitigating pilot contamination in massive mimo part i theory and channel estimation"
    - "training design and channel estimation in uplink cloud radio access networks"
    - "joint power and antenna selection optimization in large cloud radio access networks"
    - "dynamic nested clustering for parallel phy layer processing in cloud rans"
    - "blind pilot decontamination"

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)