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: "millimeter wave beam selection using out of band spatial information"
  Abstract: Millimeter wave (mmWave) communication is one feasible solution for high data-rate applications like vehicular-to-everything communication and next generation cellular communication. Configuring mmWave links, which can be done through channel estimation or beam-selection, however, is a source of significant overhead. In this paper, we propose to use spatial information extracted at sub-6 GHz to help establish the mmWave link. First, we review the prior work on frequency dependent channel behavior and outline a simulation strategy to generate multi-band frequency dependent channels. Second, assuming: (i) narrowband channels and a fully digital architecture at sub-6 GHz; and (ii) wideband frequency selective channels, OFDM signaling, and an analog architecture at mmWave, we outline strategies to incorporate sub-6 GHz spatial information in mmWave compressed beam selection. We formulate compressed beam-selection as a weighted sparse signal recovery problem, and obtain the weighting information from sub-6 GHz channels. In addition, we outline a structured precoder/combiner design to tailor the training to out-of-band information. We also extend the proposed out-of-band aided compressed beam-selection approach to leverage information from all active OFDM subcarriers. The simulation results for achievable rate show that out-of-band aided beam-selection can reduce the training overhead of in-band only beam-selection by 4x.

Connected through hyperedge HE-1 (induced by source paper "deep learning for mmwave beam and blockage prediction using sub 6ghz channels"):
  with members:
    - "deep learning for tdd and fdd massive mimo mapping channels in space and frequency"
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "deep learning and gaussian process based band assignment in dual band systems"
    - "deep learning for direct hybrid precoding in millimeter wave massive mimo systems"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "machine learning for reliable mmwave systems blockage prediction and proactive handoff"
    - "spatial covariance estimation for millimeter wave hybrid systems using out of band information"
    - "modeling and analyzing millimeter wave cellular systems"

Connected through hyperedge HE-2 (induced by source paper "deep learning predictive band switching in wireless networks"):
  with members:
    - "deep learning for tdd and fdd massive mimo mapping channels in space and frequency"
    - "beam codebook design for 5g mmwave terminals"
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "partially blind handovers for mmwave new radio aided by sub 6 ghz lte signaling"
    - "spatial covariance estimation for millimeter wave hybrid systems using out of band information"
    - "caching meets millimeter wave communications for enhanced mobility management in 5g networks"
    - "improved handover through dual connectivity in 5g mmwave mobile networks"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-3 (induced by source paper "passive radar at the roadside unit to configure millimeter wave vehicle to infrastructure links"):
  with members:
    - "an approximate message passing framework for side information"
    - "spatial covariance estimation for millimeter wave hybrid systems using out of band information"
    - "fdd massive mimo via ul dl channel covariance extrapolation and active channel sparsification"
    - "spatial channel covariance estimation for the hybrid mimo architecture a compressive sensing based approach"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "channel covariance estimation in massive mimo frequency division duplex systems"
    - "frequency selective hybrid precoding for limited feedback millimeter wave systems"
    - "hybrid beamforming for massive mimo a survey"

Connected through hyperedge HE-4 (induced by source paper "dual band fading multiple access relay channels"):
  with members:
    - "gaussian 1 2 1 networks capacity results for mmwave communications"
    - "secure communication over 1 2 1 networks"
    - "beam management for millimeter wave beamspace mu mimo systems"
    - "downlink and uplink cell association with traditional macrocells and millimeter wave small cells"
    - "link regime and power savings of decode forward relaying in fading channels"
    - "joint millimeter wave and microwave resources allocation in cellular networks with dual mode base stations"
    - "hybrid millimeter wave systems a novel paradigm for hetnets"

Connected through hyperedge HE-5 (induced by source paper "towards 6g networks use cases and technologies"):
  with members:
    - "6g the next frontier"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "machine learning for networking workflow advances and opportunities"
    - "towards 5g enabled tactile robotic telesurgery"
    - "millimeter wave vehicular communication to support massive automotive sensing"

Connected through hyperedge HE-6 (induced by source paper "predicting device to device channels from cellular channel measurements a learning approach"):
  with members:
    - "enabling fdd massive mimo through deep learning based channel prediction"
    - "mode selection resource allocation and power control for d2d enabled two tier cellular network"

Connected through hyperedge HE-7 (induced by source paper "fdd massive mimo via ul dl channel covariance extrapolation and active channel sparsification"):
  with members:
    - "multi band covariance interpolation with applications in massive mimo"
    - "fdd massive mimo efficient downlink probing and uplink feedback via active channel sparsification"
    - "channel estimation for tdd fdd massive mimo systems with channel covariance computing"
    - "on the ergodic rate lower bounds with applications to massive mimo"
    - "dictionary learning based sparse channel representation and estimation for fdd massive mimo systems"
    - "low rank covariance assisted downlink training and channel estimation for fdd massive mimo systems"
    - "the world s first real time testbed for massive mimo design implementation and validation"
    - "on the asymptotic equivalence of circulant and toeplitz matrices"

Connected through hyperedge HE-8 (induced by source paper "bandit inspired beam searching scheme for mmwave high speed train communications"):
  with members:
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "beam on graph simultaneous channel estimation for mmwave mimo systems with multiple users"
    - "multi user precoding and channel estimation for hybrid millimeter wave systems"
    - "millimeter wave mimo channel estimation using overlapped beam patterns and rate adaptation"
    - "hybrid digital and analog beamforming design for large scale antenna arrays"
    - "wireless communications in the era of big data"
    - "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)