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: "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.

Connected through hyperedge HE-1 (induced by source paper "reinforcement learning based vehicle cell association algorithm for highly mobile millimeter wave communication"):
  with members:
    - "vehicular edge computing via deep reinforcement learning"
    - "wireless edge computing with latency and reliability guarantees"
    - "learning to optimize training deep neural networks for interference management"
    - "federated learning for ultra reliable low latency v2v communications"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "user association and load balancing for massive mimo through deep learning"
    - "load balancing user association in millimeter wave mimo networks"

Connected through hyperedge HE-2 (induced by source paper "constrained thompson sampling for wireless link optimization"):
  with members:
    - "beyond the click through rate web link selection with multi level feedback"
    - "unimodal thompson sampling for graph structured arms"
    - "dynamic rate and channel selection in cognitive radio systems"
    - "bandits with knapsacks"
    - "optimal rate sampling in 802 11 systems"
    - "further optimal regret bounds for thompson sampling"
    - "coding versus arq in fading channels how reliable should the phy be"
    - "multi armed bandits in metric spaces"

Connected through hyperedge HE-3 (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"
    - "optimal beam sweeping and communication in mobile millimeter wave networks"
    - "throughput optimal beam alignment in millimeter wave networks"
    - "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-4 (induced by source paper "energy aware multi server mobile edge computing a deep reinforcement learning approach"):
  with members:
    - "an incentive aware job offloading control framework for mobile edge computing"
    - "decentralized computation offloading for multi user mobile edge computing a deep reinforcement learning approach"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "moth mobility induced outages in thz a beyond 5g b5g application"):
  with members:
    - "parameter modeling for small scale mobility in indoor thz communication"
    - "inferbeam a fast beam alignment protocol for millimeter wave networking"
    - "mobility management for heterogeneous networks leveraging millimeter wave for seamless handover"

Connected through hyperedge HE-6 (induced by source paper "integrating sub 6 ghz and millimeter wave to combat blockage delay optimal scheduling"):
  with members:
    - "empirical effects of dynamic human body blockage in 60 ghz communications"
    - "energy efficient power and bandwidth allocation in an integrated sub 6 ghz millimeter wave system"

Connected through hyperedge HE-7 (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"
    - "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"
    - "an overview of signal processing techniques for millimeter wave mimo systems"

Connected through hyperedge HE-8 (induced by source paper "beam discovery using linear block codes for millimeter wave communication networks"):
  with members:
    - "agile millimeter wave networks with provable guarantees"
    - "millimeter wave mimo channel estimation based on adaptive compressed sensing"
    - "linear block coding for efficient beam discovery in millimeter wave communication networks"
    - "a compressive channel estimation technique robust to synchronization impairments"
    - "millimeter wave mimo channel estimation using overlapped beam patterns and rate adaptation"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "low power analog to digital conversion in millimeter wave systems impact of resolution and bandwidth on performance"
    - "compressed sensing for wireless communications useful tips and tricks"

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)