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 "active learning and csi acquisition for mmwave initial alignment"):
  Abstract: Millimeter wave (mmWave) communication with large antenna arrays is a promising technique to enable extremely high data rates due to large available bandwidth in mmWave frequency bands. In addition, given the knowledge of an optimal directional beamforming vector, large antenna arrays have been shown to overcome both the severe signal attenuation in mmWave as well as the interference problem. However, fundamental limits on achievable learning rate of an optimal beamforming vector remain. This paper considers the problem of adaptive and sequential optimization of the beamforming vectors during the initial access phase of communication. With a single-path channel model, the problem is reduced to actively learning the Angle-of-Arrival (AoA) of the signal sent from the user to the Base Station (BS). Drawing on the recent results in the design of a hierarchical beamforming codebook, sequential measurement dependent noisy search strategies, and active learning from an imperfect labeler, an adaptive and sequential alignment algorithm is proposed. For any given resolution and error probability of the estimated AoA, an upper bound on the expected search time of the proposed algorithm is derived via Extrinsic Jensen-Shannon Divergence. The upper bound demonstrates that the search time of the proposed algorithm asymptotically matches the performance of the noiseless bisection search up to a constant factor, in effect, characterizing the AoA acquisition rate. Furthermore, the upper bound shows that the acquired AoA error probability decays exponentially fast with the search time with an exponent that is a decreasing function of the acquisition rate. Numerically, the proposed algorithm is compared with prior work where a significant improvement of the system communication rate is observed. Most notably, in the relevant regime of low (−10 dB to +5 dB) raw SNR, this establishes the first practically viable solution for initial access and, hence, the first demonstration of stand-alone mmWave communication.

Includes members:
  Member "improved target acquisition rates with feedback codes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "resolution limits of noisy 20 questions estimation")
  Member "linear block coding for efficient beam discovery in millimeter wave communication networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "source coding based mmwave channel estimation with deep learning based decoding")
  Member "overview of millimeter wave communications for fifth generation 5g wireless networks with a focus on propagation models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "suburban fixed wireless access channel measurements and models at 28 ghz for 90 outdoor coverage")
  Member "comparative analysis of initial access techniques in 5g mmwave cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on optimal multi user beam alignment in millimeter wave wireless systems")
  Member "hybrid beamforming for massive mimo a survey":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "amplitude and phase estimation for absolute calibration of massive mimo front ends")
  Member "active learning from imperfect labelers":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning in confusion batch active learning with noisy oracle")
  Member "an efficient feedback coding scheme with low error probability for discrete memoryless channels":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "construction and analysis of posterior matching in arbitrary dimensions via optimal transport")
  Member "a simple proof for the optimality of randomized posterior matching":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "construction and analysis of posterior matching in arbitrary dimensions via optimal transport")

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)