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 "faster activity and data detection in massive random access a multi armed bandit approach"):
  Abstract: This paper investigates the grant-free random access with massive IoT devices. By embedding the data symbols in the signature sequences, joint device activity detection and data decoding can be achieved, which, however, significantly increases the computational complexity. Coordinate descent algorithms that enjoy a low per-iteration complexity have been employed to solve the detection problem, but previous works typically employ a random coordinate selection policy which leads to slow convergence. In this paper, we develop multi-armed bandit approaches for more efficient detection via coordinate descent, which make a delicate trade-off between exploration and exploitation in coordinate selection. Specifically, we first propose a bandit based strategy, i.e., Bernoulli sampling, to speed up the convergence rate of coordinate descent, by learning which coordinates will result in more aggressive descent of the objective function. To further improve the convergence rate, an inner multi-armed bandit problem is established to learn the exploration policy of Bernoulli sampling. Both convergence rate analysis and simulation results are provided to show that the proposed bandit based algorithms enjoy faster convergence rates with a lower time complexity compared with the state-of-the-art algorithm. Furthermore, our proposed algorithms are applicable to different scenarios, e.g., massive random access with low-precision analog-to-digital converters (ADCs).

Includes members:
  Member "phase transition analysis for covariance based massive random access with massive mimo":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "grant free massive random access with a massive mimo receiver")
  Member "the roadmap to 6g ai empowered wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "when wireless security meets machine learning motivation challenges and research directions")
  Member "improved scaling law for activity detection in massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a dimension reduction based joint activity detection and channel estimation algorithm for massive access")
  Member "joint activity detection and channel estimation for iot networks phase transition and computation estimation tradeoff":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bayesian receiver design for grant free noma with message passing based structured signal estimation")
  Member "grant free massive mtc enabled massive mimo a compressive sensing approach":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a dimension reduction based joint activity detection and channel estimation algorithm for massive access")
  Member "machine learning for combinatorial optimization a methodological tour d horizon":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lossless compression of deep neural networks")
  Member "sparse activity detection for massive connectivity":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a dimension reduction based joint activity detection and channel estimation algorithm for massive access")
  Member "massive connectivity with massive mimo part ii achievable rate characterization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "random access for massive machine type communications")

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.LG(Machine Learning)