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 "deep learning and gaussian process based band assignment in dual band systems"):
  Abstract: We consider the band assignment (BA) problem in dual-band systems, where the basestation (BS) chooses one of the two available frequency bands (centimeter-wave and millimeter-wave bands) to communicate with the user equipment (UE). While the millimeter-wave band might offer higher data rate, there is a significant probability of outage during which the communication should be carried on the (more reliable) centimeter-wave band. We consider two variations of the BA problem, one-shot and sequential BA. For the former the BS uses only the currently observed information to decide whether to switch to the other frequency band, for the sequential BA, the BS uses a window of previously observed information to predict the best band for a future time step. We provide two approaches to solve the BA problem, (i) a deep learning approach that is based on Long Short Term Memory and/or multi-layer Neural Networks, and (ii) a Gaussian Process based approach, which relies on the assumption that the channel states are jointly Gaussian. We compare the achieved performances to several benchmarks in two environments: (i) a stochastic environment, and (ii) microcellular outdoor channels obtained by ray-tracing. In general, the deep learning solution shows superior performance in both environments.

Includes members:
  Member "partially blind handovers for mmwave new radio aided by sub 6 ghz lte signaling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep reinforcement learning for 5g networks joint beamforming power control and interference coordination")
  Member "machine learning for reliable mmwave systems blockage prediction and proactive handoff":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adaptive millimeter wave communications exploiting mobility and blockage dynamics")
  Member "classification algorithms for semi blind uplink downlink decoupling in sub 6 ghz mmwave 5g networks":
    also connected through other hyperedges:
      (none)
  Member "spatial covariance estimation for millimeter wave hybrid systems using out of band information":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep learning for mmwave beam and blockage prediction using sub 6ghz channels")
  Member "an introduction to deep learning for the physical layer":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")
  Member "deep learning for wireless physical layer opportunities and challenges":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a deep learning framework for optimization of miso downlink beamforming")
  Member "artificial neural networks based machine learning for wireless networks a tutorial":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fog computing systems state of the art research issues and future trends")
  Member "remote channel inference for beamforming in ultra dense hyper cellular network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "inferring remote channel state information cram er rao lower bound and deep learning implementation")

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)