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: "energy efficient interactive beam alignment for millimeter wave networks"
  Abstract: Millimeter-wave will be a key technology in next-generation wireless networks thanks to abundant bandwidth availability. However, the use of large antenna arrays with beamforming demands precise beam-alignment between transmitter and receiver, and may entail huge overhead in mobile environments. This paper investigates the design of an optimal interactive beam-alignment and data communication protocol, with the goal of minimizing power consumption under a minimum rate constraint. The base-station selects beam-alignment or data communication and the beam parameters, based on feedback from the user-end. Based on the sectored antenna model and uniform prior on the angles of departure and arrival (AoD/AoA), the optimality of a fixed-length beam-alignment phase followed by a data-communication phase is demonstrated. Moreover, a decoupled fractional beam-alignment method is shown to be optimal, which decouples over time the alignment of AoD and AoA, and iteratively scans a fraction of their region of uncertainty. A heuristic policy is proposed for non-uniform prior on AoD/AoA, with provable performance guarantees, and it is shown that the uniform prior is the worst-case scenario. The performance degradation due to detection errors is studied analytically and via simulation. The numerical results with analog beams depict up to 4dB, 7.5dB, and 14 dB gains over a state-of-the-art bisection method, conventional and interactive exhaustive search policies, respectively, and demonstrate that the sectored model provides valuable insights for beam-alignment design.

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"
    - "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"
    - "optimal beam sweeping and communication in mobile millimeter wave 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"
    - "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"
    - "optimal beam sweeping and communication in mobile millimeter wave 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 "joint path selection and rate allocation framework for 5g self backhauled mm wave networks"):
  with members:
    - "path selection and rate allocation in self backhauled mmwave networks"
    - "ultra reliable communication in 5g mmwave networks a risk sensitive approach"
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "toward low latency and ultra reliable virtual reality"
    - "millimeter wave v2v communications distributed association and beam alignment"
    - "capacity scaling of cellular networks impact of bandwidth infrastructure density and number of antennas"
    - "joint load balancing and interference mitigation in 5g heterogeneous networks"
    - "ultra reliable and low latency communication in mmwave enabled massive mimo networks"

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"
    - "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"
    - "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"

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)