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 for mmwave massive mimo based access and backhaul in ultra dense network"
  Abstract: Millimeter-wave (mmWave) massive MIMO used for access and backhaul in ultra-dense network (UDN) has been considered as the promising 5G technique. We consider such an heterogeneous network (HetNet) that ultra-dense small base stations (BSs) exploit mmWave massive MIMO for access and backhaul, while macrocell BS provides the control service with low frequency band. However, the channel estimation for mmWave massive MIMO can be challenging, since the pilot overhead to acquire the channels associated with a large number of antennas in mmWave massive MIMO can be prohibitively high. This paper proposes a structured compressive sensing (SCS)-based channel estimation scheme, where the angular sparsity of mmWave channels is exploited to reduce the required pilot overhead. Specifically, since the path loss for non-line-of-sight paths is much larger than that for line-of-sight paths, the mmWave massive channels in the angular domain appear the obvious sparsity. By exploiting such sparsity, the required pilot overhead only depends on the small number of dominated multipath. Moreover, the sparsity within the system bandwidth is almost unchanged, which can be exploited for the further improved performance. Simulation results demonstrate that the proposed scheme outperforms its counterpart, and it can approach the performance bound.

Connected through hyperedge HE-1 (induced by source paper "fully partially connected hybrid beamforming architectures for mmwave mu mimo"):
  with members:
    - "hybrid precoding architecture for massive multiuser mimo with dissipation sub connected or fully connected structures"
    - "on the energy efficiency of mimo hybrid beamforming for millimeter wave systems with nonlinear power amplifiers"
    - "hybrid beamforming based on implicit channel state information for millimeter wave links"
    - "fully connected vs sub connected hybrid precoding architectures for mmwave mu mimo"
    - "millimeter wave mimo prototype measurements and experimental results"
    - "hybrid analog digital beamforming for massive mimo systems"
    - "frequency domain compressive channel estimation for frequency selective hybrid mmwave mimo systems"
    - "a robust time domain beam alignment scheme for multi user wideband mmwave systems"

Connected through hyperedge HE-2 (induced by source paper "matrix completion based channel estimation for mmwave communication systems with array inherent impairments"):
  with members:
    - "millimeter wave channel estimation via exploiting joint sparse and low rank structures"
    - "alternating minimization algorithms for hybrid precoding in millimeter wave mimo systems"
    - "channel estimation for millimeter wave multiuser mimo systems via parafac decomposition"
    - "channel estimation for hybrid architecture based wideband millimeter wave systems"
    - "subspace estimation and decomposition for large millimeter wave mimo systems"
    - "codebook design for millimeter wave channel estimation with hybrid precoding structure"
    - "joint csit acquisition based on low rank matrix completion for fdd massive mimo systems"
    - "hybrid analog digital channel estimation and beamforming training throughput tradeoff"

Connected through hyperedge HE-3 (induced by source paper "millimeter wave mimo channel estimation based on adaptive compressed sensing"):
  with members:
    - "study on 3gpp rural macrocell path loss models for millimeter wave wireless communications"
    - "a novel millimeter wave channel simulator and applications for 5g wireless communications"
    - "alternating minimization algorithms for hybrid precoding in millimeter wave mimo systems"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "compressed sensing based multi user millimeter wave systems how many measurements are needed"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "what will 5g be"
    - "spatially sparse precoding in millimeter wave mimo systems"

Connected through hyperedge HE-4 (induced by source paper "frequency domain compressive channel estimation for frequency selective hybrid mmwave mimo systems"):
  with members:
    - "channel estimation for millimeter wave massive mimo with hybrid precoding over frequency selective fading channels"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "near optimal adaptive compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "a robust time domain beam alignment scheme for multi user wideband mmwave systems"):
  with members:
    - "a scalable and statistically robust beam alignment technique for mm wave systems"
    - "channel estimation for hybrid architecture based wideband millimeter wave systems"
    - "compressed sensing based multi user millimeter wave systems how many measurements are needed"

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)