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: "blind estimation of sparse broadband massive mimo channels with ideal and one bit adcs"
  Abstract: We study the maximum likelihood problem for the blind estimation of massive mmWave MIMO channels while taking into account their underlying sparse structure, the temporal shifts across antennas in the broadband regime, and ultimately one-bit quantization at the receiver. The sparsity in the angular domain is exploited as a key property to enable the unambiguous blind separation between user's channels. The main advantage of this approach is the fact that the overhead due to pilot sequences can be dramatically reduced especially when operating at low SNR per antenna. In addition, as sparsity is the only assumption made about the channel, the proposed method is robust with respect to the statistical properties of the channel and data and allows the channel estimation and the separation of interfering users from adjacent base stations to be performed in rapidly time-varying scenarios. For the case of one-bit receivers, a blind channel estimation is proposed that relies on the Expectation Maximization (EM) algorithm. Additionally, performance limits are derived based on the clairvoyant Cramer Rao lower bound. Simulation results demonstrate that this maximum likelihood formulation yields superior estimation accuracy in the narrowband as well as the wideband regime with reasonable computational complexity and limited model assumptions.

Connected through hyperedge HE-1 (induced by source paper "joint channel estimation decoding with frequency selective channels and few bit adcs"):
  with members:
    - "throughput analysis of massive mimo uplink with low resolution adcs"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "phase diagram of matrix compressed sensing"
    - "parametric bilinear generalized approximate message passing"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "sparse channel estimation for massive mimo with 1 bit feedback per dimension"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"

Connected through hyperedge HE-2 (induced by source paper "super resolution blind channel and signal estimation for massive mimo with one dimensional antenna array"):
  with members:
    - "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"
    - "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-3 (induced by source paper "a case for digital beamforming at mmwave"):
  with members:
    - "a tutorial on beam management for 3gpp nr at mmwave frequencies"
    - "throughput analysis of massive mimo uplink with low resolution adcs"
    - "5g millimeter wave cellular system capacity with fully digital beamforming"
    - "uplink performance of wideband massive mimo with one bit adcs"
    - "hybrid architectures with few bit adc receivers achievable rates and energy rate tradeoffs"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"
    - "low power analog to digital conversion in millimeter wave systems impact of resolution and bandwidth on performance"
    - "capacity analysis of one bit quantized mimo systems with transmitter channel state information"

Connected through hyperedge HE-4 (induced by source paper "on the uplink achievable rate for massive mimo with 1 bit adc and superimposed pilots"):
  with members:
    - "throughput analysis of massive mimo uplink with low resolution adcs"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "superimposed pilots are superior for mitigating pilot contamination in massive mimo"
    - "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"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "low power analog to digital conversion in millimeter wave systems impact of resolution and bandwidth on performance"

Connected through hyperedge HE-5 (induced by source paper "super resolution blind channel and signal estimation for massive mimo with arbitrary array geometry"):
  with members:
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "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"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "joint spatial division and multiplexing for mm wave channels"
    - "expectation maximization gaussian mixture approximate message passing"

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)