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 "efficient beam alignment in millimeter wave systems using contextual bandits"):
  Abstract: In this paper, we investigate the problem of beam alignment in millimeter wave (mmWave) systems, and design an optimal algorithm to reduce the overhead. Specifically, due to directional communications, the transmitter and receiver beams need to be aligned, which incurs high delay overhead since without a priori knowledge of the transmitter/receiver location, the search space spans the entire angular domain. This is further exacerbated under dynamic conditions (e.g., moving vehicles) where the access to the base station (access point) is highly dynamic with intermittent on-off periods, requiring more frequent beam alignment and signal training. To mitigate this issue, we consider an online stochastic optimization formulation where the goal is to maximize the directivity gain (i.e., received energy) of the beam alignment policy within a time period. We exploit the inherent correlation and unimodality properties of the model, and demonstrate that contextual information improves the performance. To this end, we propose an equivalent structured Multi-Armed Bandit model to optimally exploit the exploration-exploitation tradeoff. In contrast to the classical MAB models, the contextual information makes the lower bound on regret (i.e., performance loss compared with an oracle policy) independent of the number of beams. This is a crucial property since the number of all combinations of beam patterns can be large in transceiver antenna arrays, especially in massive MIMO systems. We further provide an asymptotically optimal beam alignment algorithm, and investigate its performance via simulations.

Includes members:
  Member "agile millimeter wave networks with provable guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "association blockage and handoffs in ieee 802 11ad based 60 ghz picocells a closer look")
  Member "millimeter wave beam selection using out of band spatial information":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep learning for mmwave beam and blockage prediction using sub 6ghz channels")
  Member "energy efficient power and bandwidth allocation in an integrated sub 6 ghz millimeter wave system":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "integrating sub 6 ghz and millimeter wave to combat blockage delay optimal scheduling")
  Member "coordinated initial access in millimetre wave standalone networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a genetic algorithm based beamforming approach for delay constrained networks")
  Member "unimodal thompson sampling for graph structured arms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "constrained thompson sampling for wireless link optimization")
  Member "unimodal bandits regret lower bounds and optimal algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "closed form whittle s index enabled random access for timely status update")
  Member "directional cell discovery in millimeter wave cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a tutorial on beam management for 3gpp nr at mmwave frequencies")
  Member "dynamic rate and channel selection in cognitive radio systems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "constrained thompson sampling for wireless link optimization")

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)