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 "power control in cellular massive mimo with varying user activity a deep learning solution"):
  Abstract: This paper demonstrates how neural networks can be used to perform efficient joint pilot and data power control in multi-cell Massive MIMO systems, by exploiting the problem structure. We first consider the sum spectral efficiency (SE) optimization problem for systems with a dynamically varying number of active users. Since this problem is non-convex, an iterative algorithm is first derived to obtain a stationary point in polynomial time. We then use this algorithm together with deep learning to achieve an implementation that provable can be used in real-time applications. The proposed neural network, PowerNet, only uses the large-scale fading information to predict both pilot and data powers. One key feature is that PowerNet can manage a dynamically changing number of users per cell without requiring retraining, which is not the case in prior works and thus makes PowerNet an important step towards a practically useful solution. Numerical results demonstrate that PowerNet only loses $2\%$ in sum SE, compared to the iterative algorithm, in a nine-cell system with up to $90$ active users per in each coherence interval, and the runtime was only $0.03$ ms on a graphics processing unit (GPU).

Includes members:
  Member "sum spectral efficiency maximization in massive mimo systems benefits from deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on deep learning based techniques for modeling and estimation of massivemimo channels")
  Member "optimized power control for massive mimo with underlaid d2d communications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "joint frame design and resource allocation for ultra reliable and low latency vehicular networks")
  Member "joint transmit and circuit power minimization in massive mimo with downlink sinr constraints when to turn on massive mimo":
    also connected through other hyperedges:
      (none)
  Member "large scale fading decoding in cellular massive mimo systems with spatially correlated channels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "performance of multi cell massive mimo systems with interference decoding")
  Member "learning to optimize training deep neural networks for interference management":
    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 "model aided wireless artificial intelligence embedding expert knowledge in deep neural networks towards wireless systems optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a graph neural network approach for scalable wireless power control")
  Member "residual dense network for image super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "seeing through fog without seeing fog deep multimodal sensor fusion in unseen adverse weather")
  Member "an introduction to deep learning for the physical layer":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")

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)