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: "compressive channel estimation and tracking for large arrays in mm wave picocells"
  Abstract: We propose and investigate a compressive architecture for estimation and tracking of sparse spatial channels in millimeter (mm) wave picocellular networks. The base stations are equipped with antenna arrays with a large number of elements (which can fit within compact form factors because of the small carrier wavelength) and employ radio frequency (RF) beamforming, so that standard least squares adaptation techniques (which require access to individual antenna elements) are not applicable. We focus on the downlink, and show that “compressive beacons,” transmitted using pseudorandom phase settings at the base station array, and compressively processed using pseudorandom phase settings at the mobile array, provide information sufficient for accurate estimation of the two-dimensional (2D) spatial frequencies associated with the directions of departure of the dominant rays from the base station, and the associated complex gains. This compressive approach is compatible with coarse phase-only control, and is based on a near-optimal sequential algorithm for frequency estimation which approaches the Cramer Rao Lower Bound. The algorithm exploits the geometric continuity of the channel across successive beaconing intervals to reduce the overhead to less than 1% even for very large (  $32 \times 32$  ) arrays. Compressive beaconing is essentially omnidirectional, and hence does not enjoy the SNR and spatial reuse benefits of beamforming obtained during data transmission. We therefore discuss system level design considerations for ensuring that the beacon SNR is sufficient for accurate channel estimation, and that inter-cell beacon interference is controlled by an appropriate reuse scheme.

Connected through hyperedge HE-1 (induced by source paper "mobility and blockage aware communications in millimeter wave vehicular networks"):
  with members:
    - "adaptive millimeter wave communications exploiting mobility and blockage dynamics"
    - "second best beam alignment via bayesian multi armed bandits"
    - "active learning and csi acquisition for mmwave initial alignment"
    - "energy efficient interactive beam alignment for millimeter wave networks"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "online learning for position aided millimeter wave beam training"
    - "beam training and data transmission optimization in millimeter wave vehicular networks"
    - "optimal beam sweeping and communication in mobile millimeter wave networks"

Connected through hyperedge HE-2 (induced by source paper "adaptive millimeter wave communications exploiting mobility and blockage dynamics"):
  with members:
    - "mobility and blockage aware communications in millimeter wave vehicular networks"
    - "second best beam alignment via bayesian multi armed bandits"
    - "energy efficient interactive beam alignment for millimeter wave networks"
    - "online learning for position aided millimeter wave beam training"
    - "machine learning for reliable mmwave systems blockage prediction and proactive handoff"
    - "beam training and data transmission optimization in millimeter wave vehicular networks"
    - "optimal beam sweeping and communication in mobile millimeter wave networks"
    - "inverse multipath fingerprinting for millimeter wave v2i beam alignment"

Connected through hyperedge HE-3 (induced by source paper "swift link a compressive beam alignment algorithm for practical mmwave radios"):
  with members:
    - "partially blind handovers for mmwave new radio aided by sub 6 ghz lte signaling"
    - "a novel millimeter wave channel simulator and applications for 5g wireless communications"
    - "a compressive channel estimation technique robust to synchronization impairments"
    - "hierarchical codebook design for beamforming training in millimeter wave communication"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "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"
    - "self calibration and biconvex compressive sensing"

Connected through hyperedge HE-4 (induced by source paper "compressed sensing channel estimation for ofdm with non gaussian multipath gains"):
  with members:
    - "fast channel estimation for millimetre wave wireless systems using overlapped beam patterns"
    - "average performance of orthogonal matching pursuit omp for sparse approximation"
    - "channel estimation for millimeter wave massive mimo with hybrid precoding over frequency selective fading channels"
    - "channel estimation for hybrid architecture based wideband millimeter wave systems"
    - "compressed sensing algorithms for ofdm channel estimation"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"
    - "limited feedback hybrid precoding for multi user millimeter wave systems"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"

Connected through hyperedge HE-5 (induced by source paper "multidimensional variational line spectra estimation"):
  with members:
    - "grid less variational bayesian inference of line spectral from quantized samples"
    - "efficient downlink channel reconstruction for fdd multi antenna systems"
    - "variational bayesian inference of line spectra"
    - "vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional super resolution"
    - "robust spectral compressed sensing via structured matrix completion"

Connected through hyperedge HE-6 (induced by source paper "channel fingerprint based beam tracking for millimeter wave communications"):
  with members:
    - "inverse multipath fingerprinting for millimeter wave v2i beam alignment"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "tracking mm wave channel dynamics fast beam training strategies under mobility"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"

Connected through hyperedge HE-7 (induced by source paper "tracking position and orientation through millimeter wave lens mimo in 5g systems"):
  with members:
    - "optimal precoders for tracking the aod and aoa of a mmwave path"
    - "direct localization for massive mimo"
    - "error bounds for uplink and downlink 3d localization in 5g mmwave systems"
    - "multi user millimeter wave mimo with full dimensional lens antenna array"
    - "joint device positioning and clock synchronization in 5g ultra dense networks"
    - "frequency selective hybrid precoding for limited feedback millimeter wave systems"
    - "tracking angles of departure and arrival in a mobile millimeter wave channel"
    - "fingerprinting based positioning in distributed massive mimo systems"

Connected through hyperedge HE-8 (induced by source paper "message passing based joint cfo and channel estimation in millimeter wave systems with one bit adcs"):
  with members:
    - "partially blind handovers for mmwave new radio aided by sub 6 ghz lte signaling"
    - "joint cfo and channel estimation in millimeter wave systems with one bit adcs"
    - "a compressive channel estimation technique robust to synchronization impairments"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "parametric bilinear generalized approximate message passing"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "channel estimation for hybrid architecture based wideband millimeter wave systems"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"

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)