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 "online learning for position aided millimeter wave beam training"):
  Abstract: Accurate beam alignment is essential for beam-based millimeter wave communications. Conventional beam sweeping solutions often have large overhead, which is unacceptable for mobile applications like vehicle-to-everything. Learning-based solutions that leverage sensor data like position to identify good beam directions are one approach to reduce the overhead. Most existing solutions, though, are supervised-learning where the training data is collected beforehand. In this paper, we use a multi-armed bandit framework to develop online learning algorithms for beam pair selection and refinement. The beam pair selection algorithm learns coarse beam directions in some predefined beam codebook, e.g., in discrete angles separated by the 3 dB beamwidths. The beam refinement fine-tunes the identified directions to match the peak of the power angular spectrum at that position. The beam pair selection uses the upper confidence bound (UCB) with a newly proposed risk-aware feature, while the beam refinement uses a modified optimistic optimization algorithm. The proposed algorithms show a fast learning behavior. When using 16x16 arrays at both the transmitter and receiver, it can achieve on average 1 dB gain over the exhaustive search (over 271x271 beam pairs) on the unrefined codebook within 100 time-steps with a training budget of only 30 beam pairs.

Includes members:
  Member "mmwave beam prediction with situational awareness a machine learning approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "wireless edge computing with latency and reliability guarantees")
  Member "deep learning coordinated beamforming for highly mobile millimeter wave systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a deep learning framework for optimization of miso downlink beamforming")
  Member "beam training and data transmission optimization in millimeter wave vehicular networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mobility and blockage aware communications in millimeter wave vehicular networks")
  Member "robust location aided beam alignment in millimeter wave massive mimo":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimal precoders for tracking the aod and aoa of a mmwave path")
  Member "efficient beam alignment in millimeter wave systems using contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "reinforcement learning based vehicle cell association algorithm for highly mobile millimeter wave communication")
  Member "inverse multipath fingerprinting for millimeter wave v2i beam alignment":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mobility and blockage aware communications in millimeter wave vehicular networks")
  Member "compressive channel estimation and tracking for large arrays in mm wave picocells":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mobility and blockage aware communications in millimeter wave vehicular networks")
  Member "millimeter wave vehicular communication to support massive automotive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "mobility and blockage aware communications in millimeter wave vehicular networks")

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)