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: "linear block coding for efficient beam discovery in millimeter wave communication networks"
  Abstract: The surge in mobile broadband data demands is expected to surpass the available spectrum capacity below 6 GHz. This expectation has prompted the exploration of millimeter wave (mm-wave) frequency bands as a candidate technology for next generation wireless networks. However, numerous challenges to deploying mm-wave communication systems, including channel estimation, need to be met before practical deployments are possible. This work addresses the mm-wave channel estimation problem and treats it as a beam discovery problem in which locating beams with strong path reflectors is analogous to locating errors in linear block codes. We show that a significantly small number of measurements (compared to the original dimensions of the channel matrix) is sufficient to reliably estimate the channel. We also show that this can be achieved using a simple and energy-efficient transceiver architecture.

Connected through hyperedge HE-1 (induced by source paper "active learning and csi acquisition for mmwave initial alignment"):
  with members:
    - "improved target acquisition rates with feedback codes"
    - "overview of millimeter wave communications for fifth generation 5g wireless networks with a focus on propagation models"
    - "comparative analysis of initial access techniques in 5g mmwave cellular networks"
    - "hybrid beamforming for massive mimo a survey"
    - "active learning from imperfect labelers"
    - "an efficient feedback coding scheme with low error probability for discrete memoryless channels"
    - "a simple proof for the optimality of randomized posterior matching"
    - "the impact of beamwidth on temporal channel variation in vehicular channels and its implications"

Connected through hyperedge HE-2 (induced by source paper "source coding based mmwave channel estimation with deep learning based decoding"):
  with members:
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "deep learning for wireless physical layer opportunities and challenges"
    - "millimeter wave mimo channel estimation using overlapped beam patterns and rate adaptation"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"
    - "tensorflow a system for large scale machine learning"
    - "compressed sensing for wireless communications useful tips and tricks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (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"
    - "efficient beam alignment in millimeter wave systems using contextual bandits"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "coded energy efficient beam alignment for millimeter wave networks"):
  with members:
    - "optimal beam sweeping and communication in mobile millimeter wave networks"
    - "throughput optimal beam alignment in millimeter wave networks"
    - "coverage and rate analysis for millimeter wave cellular networks"
    - "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)