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: "training beam sequence design for millimeter wave mimo systems a pomdp framework"
  Abstract: In this paper, adaptive training beam sequence design for efficient channel estimation in large millimeter-wave(mmWave) multiple-input multiple-output (MIMO) channels is considered. By exploiting the sparsity in large mmWave MIMO channels and imposing a Markovian random walk assumption on the movement of the receiver and reflection clusters, the adaptive training beam sequence design and channel estimation problem is formulated as a partially observableMarkov decision process (POMDP) problem that finds non-zero bins in a two-dimensional grid. Under the proposed POMDP framework, optimal and suboptimal adaptive training beam sequence design policies are derived. Furthermore, a very fast suboptimal greedy algorithm is developed based on a newly proposed reduced sufficient statistic to make the computational complexity of the proposed algorithm low to a level for practical implementation. Numerical results are provided to evaluate the performance of the proposed training beam design method. Numerical results show that the proposed training beam sequence design algorithms yield good performance.

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"
    - "robust location aided beam alignment in millimeter wave massive mimo"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "energy efficient interactive beam alignment for millimeter wave networks"):
  with members:
    - "coded energy efficient beam alignment for millimeter wave networks"
    - "optimal beam sweeping and communication in mobile millimeter wave networks"
    - "throughput optimal beam alignment in millimeter wave networks"
    - "on the temporal effects of mobile blockers in urban millimeter wave cellular scenarios"
    - "inverse multipath fingerprinting for millimeter wave v2i beam alignment"
    - "compressive channel estimation and tracking for large arrays in mm wave picocells"
    - "design and analysis of initial access in millimeter wave cellular networks"
    - "beamforming tradeoffs for initial ue discovery in millimeter wave mimo systems"

Connected through hyperedge HE-3 (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"
    - "robust location aided beam alignment in millimeter wave massive mimo"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "efficient beam training and channel estimation for millimeter wave communications under mobility"):
  with members:
    - "compressive channel estimation and tracking for large arrays in mm wave picocells"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "tracking mm wave channel dynamics fast beam training strategies under mobility"
    - "compressed sensing for wireless communications useful tips and tricks"
    - "tracking angles of departure and arrival in a mobile millimeter wave channel"
    - "a survey of millimeter wave mmwave communications for 5g opportunities and challenges"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "spatially sparse precoding in millimeter wave mimo systems"

Connected through hyperedge HE-5 (induced by source paper "a high accuracy adaptive beam training algorithm for mmwave communication"):
  with members:
    - "millimeter wave beam alignment large deviations analysis and design insights"
    - "millimeter wave mimo channel estimation using overlapped beam patterns and rate adaptation"
    - "hierarchical codebook design for beamforming training in millimeter wave communication"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "hybrid digital and analog beamforming design for large scale antenna arrays"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "what will 5g be"
    - "millimeter wave channel modeling and cellular capacity evaluation"

Connected through hyperedge HE-6 (induced by source paper "millimeter wave beam selection using out of band spatial information"):
  with members:
    - "robust location aided beam alignment in millimeter wave massive mimo"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "randomly directional beamforming in millimeter wave multi user miso downlink"):
  with members:
    - "pilot beam sequence design for channel estimation in millimeter wave mimo systems a pomdp framework"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "aspects of favorable propagation in massive mimo"
    - "a new approach to user scheduling in massive multi user mimo broadcast channels"
    - "network mimo with linear zero forcing beamforming large system analysis impact of channel estimation and reduced complexity scheduling"

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)