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: "optimal beam sweeping and communication in mobile millimeter wave networks"
  Abstract: Millimeter-wave (mm-wave) communications incur a high beam alignment cost in mobile scenarios such as vehicular networks. Therefore, an efficient beam alignment mechanism is required to mitigate the resulting overhead. In this paper, a one-dimensional mobility model is proposed where a mobile user (MU), such as a vehicle, moves along a straight road with time-varying and random speed, and communicates with base stations (BSs) located on the roadside over the mm-wave band. To compensate for location uncertainty, the BS widens its transmission beam and, when a critical beamwidth is achieved, it performs beam-sweeping to refine the MU position estimate, followed by data communication over a narrow beam. The average rate and average transmission power are computed in closed form and the optimal beamwidth for communication, number of sweeping beams, and transmission power allocation are derived so as to maximize the average rate under an average power constraint. Structural properties of the optimal design are proved, and a bisection algorithm to determine the optimal sweeping -- communication parameters is designed. It is shown numerically that an adaptation of the IEEE 802.11ad standard to the proposed model exhibits up to 90% degradation in spectral efficiency compared to the proposed 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"
    - "inverse multipath fingerprinting for millimeter wave v2i beam alignment"

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"
    - "inverse multipath fingerprinting for millimeter wave v2i beam alignment"
    - "compressive channel estimation and tracking for large arrays in mm wave picocells"

Connected through hyperedge HE-3 (induced by source paper "on optimal multi user beam alignment in millimeter wave wireless systems"):
  with members:
    - "a tutorial on beam management for 3gpp nr at mmwave frequencies"
    - "active learning and csi acquisition for mmwave initial alignment"
    - "throughput optimal beam alignment in millimeter wave networks"
    - "sectoring in multi cell massive mimo systems"
    - "resource sharing among mmwave cellular service providers in a vertically differentiated duopoly"
    - "comparative analysis of initial access techniques in 5g mmwave cellular networks"
    - "coverage and rate analysis for millimeter wave cellular networks"
    - "millimeter wave cellular wireless networks potentials and challenges"

Connected through hyperedge HE-4 (induced by source paper "second best beam alignment via bayesian multi armed bandits"):
  with members:
    - "active learning and csi acquisition for mmwave initial alignment"
    - "energy efficient interactive beam alignment for millimeter wave networks"
    - "coded energy efficient beam alignment for millimeter wave networks"
    - "throughput optimal beam alignment in millimeter wave networks"
    - "efficient beam alignment in millimeter wave systems using contextual bandits"
    - "design and analysis of initial access in millimeter wave cellular networks"
    - "the impact of beamwidth on temporal channel variation in vehicular channels and its implications"
    - "millimeter wave channel modeling and cellular capacity evaluation"

Connected through hyperedge HE-5 (induced by source paper "energy efficient interactive beam alignment for millimeter wave networks"):
  with members:
    - "coded energy efficient beam alignment for 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"
    - "the impact of beamwidth on temporal channel variation in vehicular channels and its implications"

Connected through hyperedge HE-6 (induced by source paper "beam training and data transmission optimization in millimeter wave vehicular networks"):
  with members:
    - "inverse multipath fingerprinting for millimeter wave v2i beam alignment"
    - "millimeter wave vehicular communication to support massive automotive sensing"
    - "initial access in 5g mmwave cellular networks"
    - "tracking mm wave channel dynamics fast beam training strategies under mobility"
    - "perseus randomized point based value iteration for pomdps"
    - "reinforcement learning a survey"

Connected through hyperedge HE-7 (induced by source paper "coded energy efficient beam alignment for millimeter wave networks"):
  with members:
    - "throughput optimal beam alignment in millimeter wave networks"
    - "linear block coding for efficient beam discovery in millimeter wave communication networks"
    - "coverage and rate analysis for millimeter wave cellular networks"
    - "millimeter wave channel modeling and cellular capacity evaluation"

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)