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: "robust location aided beam alignment in millimeter wave massive mimo"
  Abstract: Location-aided beam alignment has been proposed recently as a potential approach for fast link establishment in millimeter wave (mmWave) massive MIMO (mMIMO) communications. However, due to mobility and other imperfections in the estimation process, the spatial information obtained at the base station (BS) and the user (UE) is likely to be noisy, degrading beam alignment performance. In this paper, we introduce a robust beam alignment framework in order to exhibit resilience with respect to this problem. We first recast beam alignment as a decentralized coordination problem where BS and UE seek coordination on the basis of correlated yet individual position information. We formulate the optimum beam alignment solution as the solution of a Bayesian team decision problem. We then propose a suite of algorithms to approach optimality with reduced complexity. The effectiveness of the robust beam alignment procedure, compared with classical designs, is then verified on simulation settings with varying location information accuracies.

Connected through hyperedge HE-1 (induced by source paper "online learning for position aided millimeter wave beam training"):
  with members:
    - "mmwave beam prediction with situational awareness a machine learning approach"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "beam training and data transmission optimization in millimeter wave vehicular networks"
    - "efficient beam alignment in millimeter wave systems using contextual bandits"
    - "inverse multipath fingerprinting for millimeter wave v2i beam alignment"
    - "compressive channel estimation and tracking for large arrays in mm wave picocells"
    - "millimeter wave vehicular communication to support massive automotive sensing"
    - "an overview of signal processing techniques for millimeter wave mimo systems"

Connected through hyperedge HE-2 (induced by source paper "optimal precoders for tracking the aod and aoa of a mmwave path"):
  with members:
    - "fast channel estimation for millimetre wave wireless systems using overlapped beam patterns"
    - "modeling and analyzing millimeter wave cellular systems"
    - "error bounds for uplink and downlink 3d localization in 5g mmwave systems"
    - "transmitter beam selection in millimeter wave mimo with in band position aiding"
    - "hierarchical codebook design for beamforming training in millimeter wave communication"
    - "millimeter wave vehicular communication to support massive automotive sensing"
    - "channel estimation for hybrid architecture based wideband millimeter wave systems"
    - "hybrid rf and digital beamformer for cellular networks algorithms microwave architectures and measurements"

Connected through hyperedge HE-3 (induced by source paper "millimeter wave beam selection using out of band spatial information"):
  with members:
    - "inverse multipath fingerprinting for millimeter wave v2i beam alignment"
    - "millimeter wave vehicular communication to support massive automotive sensing"
    - "channel estimation for hybrid architecture based wideband millimeter wave systems"
    - "channel covariance estimation in massive mimo frequency division duplex systems"
    - "frequency selective hybrid precoding for limited feedback millimeter wave systems"
    - "beamforming tradeoffs for initial ue discovery in millimeter wave mimo systems"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "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)