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: "efficient solutions for weighted sum rate maximization in multicellular networks with channel uncertainties"
  Abstract: The important problem of weighted sum rate maximization (WSRM) in a multicellular environment is intrinsically sensitive to channel estimation errors. In this paper, we study ways to maximize the weighted sum rate in a linearly precoded multicellular downlink system where the receivers are equipped with a single antenna. With perfect channel information available at the base stations, we first present a novel fast converging algorithm that solves the WSRM problem. Then, the assumption is relaxed to the case where the error vectors in the channel estimates are assumed to lie in an uncertainty set formed by the intersection of finite ellipsoids. As our main contributions, we present two procedures to solve the intractable nonconvex robust designs based on the worst case principle. The proposed iterative algorithms solve semidefinite programs in each of their steps and provably converge to a locally optimal solution of the robust WSRM problem. The proposed solutions are numerically compared against each other and known approaches in the literature to ascertain their robustness towards channel estimation imperfections. The results clearly indicate the performance gain compared to the case when channel uncertainties are ignored in the design process. For certain scenarios, we also quantify the gap between the proposed approximations and exact solutions.

Connected through hyperedge HE-1 (induced by source paper "energy efficient processing and robust wireless cooperative transmission for edge inference"):
  with members:
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "reliable federated learning for mobile networks"
    - "edge ai on demand accelerating deep neural network inference via edge computing"
    - "two timescale hybrid compression and forward for massive mimo aided c ran"
    - "wireless network intelligence at the edge"
    - "nonconvex and nonsmooth sparse optimization via adaptively iterative reweighted methods"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "limited feedback in single and multi user mimo systems with finite bit adcs"

Connected through hyperedge HE-2 (induced by source paper "codebook based hybrid precoding for millimeter wave multiuser systems"):
  with members:
    - "distributed optimization of hierarchical small cell networks a gnep framework"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "energy efficient power control a look at 5g wireless technologies"
    - "energy efficiency optimization of 5g radio frequency chain systems"
    - "analysis of multi hop outdoor 60 ghz wireless networks with full duplex buffered relays"
    - "limited feedback hybrid precoding for multi user millimeter wave systems"
    - "hybrid mimo architectures for millimeter wave communications phase shifters or switches"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"

Connected through hyperedge HE-3 (induced by source paper "a minorization maximization method for optimizing sum rate in the downlink of non orthogonal multiple access systems"):
  with members:
    - "the application of mimo to non orthogonal multiple access"
    - "multicast multigroup beamforming for per antenna power constrained large scale arrays"
    - "fairness for non orthogonal multiple access in 5g systems"
    - "impact of user pairing on 5g non orthogonal multiple access"
    - "on the performance of non orthogonal multiple access in 5g systems with randomly deployed users"

Connected through hyperedge HE-4 (induced by source paper "sum rate and power scaling of massive mimo systems with channel aging"):
  with members:
    - "performance analysis of massive mimo for cell boundary users"
    - "uplink performance of time reversal mrc in massive mimo systems subject to phase noise"
    - "power scaling of uplink massive mimo systems with arbitrary rank channel means"
    - "pilot beam pattern design for channel estimation in massive mimo systems"
    - "massive mimo systems with non ideal hardware energy efficiency estimation and capacity limits"
    - "massive mimo for next generation wireless systems"
    - "energy efficiency optimization in hardware constrained large scale mimo systems"
    - "what will 5g be"

Connected through hyperedge HE-5 (induced by source paper "distributed bi directional training of nonlinear precoders and receivers in cellular networks"):
  with members:
    - "receive combining vs multi stream multiplexing in downlink systems with multi antenna users"
    - "downlink interference alignment"
    - "pilot contamination and precoding in multi cell tdd systems"

Connected through hyperedge HE-6 (induced by source paper "precoder design with limited feedback and backhauling for joint transmission"):
  with members:
    - "fast converging algorithm for weighted sum rate maximization in multicell miso downlink"

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)