human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "second best beam alignment via bayesian multi armed bandits"):
  Abstract: Millimeter-wave (mm-wave) systems rely on narrow-beams to cope with the severe signal attenuation in the mm-wave frequency band. However, susceptibility to beam mis-alignment due to mobility or blockage requires the use of beam-alignment schemes, with huge cost in terms of overhead and use of system resources. In this paper, a beam-alignment scheme is proposed based on Bayesian multi-armed bandits, with the goal to maximize the alignment probability and the data-communication throughput. A Bayesian approach is proposed, by considering the state as a posterior distribution over angles of arrival (AoA) and of departure (AoD), given the history of feedback signaling and of beam pairs scanned by the base-station (BS) and the user-end (UE). A simplified sufficient statistics for optimal control is derived, in the form of preference of BS-UE beam pairs. By bounding a value function, the second-best preference policy is derived, which strikes an optimal balance between exploration and exploitation by selecting the beam pair with the current second-best preference. Through Monte-Carlo simulation with analog beamforming, the superior performance of the second-best preference policy is demonstrated in comparison to existing schemes based on first-best preference, linear Thompson sampling, and upper confidence bounds, with up to 7%, 10% and 30% improvements in alignment probability, respectively.

Includes members:
  Member "active learning and csi acquisition for mmwave initial alignment":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mobility and blockage aware communications in millimeter wave vehicular networks")
  Member "energy efficient interactive beam alignment for millimeter wave networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mobility and blockage aware communications in millimeter wave vehicular networks")
  Member "coded energy efficient beam alignment for millimeter wave networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "energy efficient interactive beam alignment for millimeter wave networks")
  Member "optimal beam sweeping and communication in mobile millimeter wave networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mobility and blockage aware communications in millimeter wave vehicular networks")
  Member "throughput optimal beam alignment in millimeter wave networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on optimal multi user beam alignment in millimeter wave wireless systems")
  Member "efficient beam alignment in millimeter wave systems using contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "reinforcement learning based vehicle cell association algorithm for highly mobile millimeter wave communication")
  Member "design and analysis of initial access in millimeter wave cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sense and predict harnessing spatial interference correlation for cognitive radio networks")
  Member "the impact of beamwidth on temporal channel variation in vehicular channels and its implications":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "non uniform quantization codebook based hybrid precoding to reduce feedback overhead in millimeter wave mimo systems")

Question: Please tell me which class the source paper 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)