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: "generic optimization of linear precoding in multibeam satellite systems"
  Abstract: Multibeam satellite systems have been employed to provide interactive broadband services to geographical areas under-served by terrestrial infrastructure. In this context, this paper studies joint multiuser linear precoding design in the forward link of fixed multibeam satellite systems. We provide a generic optimization framework for linear precoding design to handle any objective functions of data rate with general linear and nonlinear power constraints. To achieve this, an iterative algorithm which optimizes the precoding vectors and power allocation alternatingly is proposed and most importantly, the proposed algorithm is proved to always converge. The proposed optimization algorithm is also applicable to nonlinear dirty paper coding. In addition, the aforementioned problems and algorithms are extended to the case that each terminal has multiple co-polarization or dual-polarization antennas. Simulation results demonstrate substantial performance improvement of the proposed schemes over conventional multibeam satellite systems, zero-forcing and regularized zero-forcing precoding schemes in terms of meeting the traffic demand. The performance of the proposed linear precoding scheme is also shown to be very close to the dirty paper coding.

Connected through hyperedge HE-1 (induced by source paper "deep learning enabled optimization of downlink beamforming under per antenna power constraints algorithms and experimental demonstration"):
  with members:
    - "a deep learning framework for optimization of miso downlink beamforming"
    - "learning to detect"
    - "artificial intelligence enabled cellular networks a critical path to beyond 5g and 6g"
    - "power control in cellular massive mimo with varying user activity a deep learning solution"
    - "learning to optimize training deep neural networks for interference management"
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"

Connected through hyperedge HE-2 (induced by source paper "rate splitting multiple access for multibeam satellite communications"):
  with members:
    - "rate splitting unifying sdma oma noma and multicasting in miso broadcast channel a simple two user rate analysis"
    - "signal processing for high throughput satellites challenges in new interference limited scenarios"
    - "rate splitting multiple access for downlink communication systems bridging generalizing and outperforming sdma and noma"
    - "rate splitting for multi antenna non orthogonal unicast and multicast transmission spectral and energy efficiency analysis"
    - "rate splitting for max min fair multigroup multicast beamforming in overloaded systems"
    - "generalized multicast multibeam precoding for satellite communications"
    - "sum rate maximization for linearly precoded downlink multiuser miso systems with partial csit a rate splitting approach"
    - "robust transmission in downlink multiuser miso systems a rate splitting approach"

Connected through hyperedge HE-3 (induced by source paper "outage behaviors of noma based satellite network over shadowed rician fading channels"):
  with members:
    - "exploiting full half duplex user relaying in noma systems"
    - "a unified framework for non orthogonal multiple access"
    - "noma assisted wireless caching strategies and performance analysis"
    - "enhancing the physical layer security of non orthogonal multiple access in large scale networks"
    - "on the performance of non orthogonal multiple access in 5g systems with randomly deployed users"

Connected through hyperedge HE-4 (induced by source paper "optimal beamforming for hybrid satellite terrestrial networks with nonlinear pa and imperfect csit"):
  with members:
    - "hybrid satellite terrestrial communication networks for the maritime internet of things key technologies opportunities and challenges"
    - "collaborative spectrum sharing for hybrid satellite terrestrial networks with large scale csi"

Connected through hyperedge HE-5 (induced by source paper "dynamic energy efficient power allocation in multibeam satellite systems"):
  with members:
    - "multibeam satellite frequency time duality study and capacity optimization"

Connected through hyperedge HE-6 (induced by source paper "precoded cluster hopping in multi beam high throughput satellite systems"):
  with members:
    - "weighted fair multicast multigroup beamforming under per antenna power constraints"

Connected through hyperedge HE-7 (induced by source paper "clustering strategies for multicast precoding in multi beam satellite systems"):
  with members:
    - "generalized multicast multibeam precoding for satellite communications"
    - "dvb s2x enabled precoding for high throughput satellite systems"
    - "multiuser detection in multibeam satellite systems theoretical analysis and practical schemes"
    - "multicast multigroup precoding and user scheduling for frame based satellite communications"
    - "weighted fair multicast multigroup beamforming under per antenna power constraints"

Connected through hyperedge HE-8 (induced by source paper "geographical scheduling for multicast precoding in multi beam satellite systems"):
  with members:
    - "clustering strategies for multicast precoding in multi beam satellite systems"
    - "dvb s2x enabled precoding for high throughput satellite systems"
    - "multiuser detection in multibeam satellite systems theoretical analysis and practical schemes"
    - "multicast multigroup precoding and user scheduling for frame based satellite communications"
    - "weighted fair multicast multigroup beamforming under per antenna power constraints"

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)