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: "alternating optimization for capacity region of gaussian mimo broadcast channels with per antenna power constraint"
  Abstract: This paper characterizes the capacity region of Gaussian MIMO broadcast channels (BCs) with per-antenna power constraint (PAPC). While the capacity region of MIMO BCs with a sum power constraint (SPC) was extensively studied, that under PAPC has received less attention. A reason is that efficient solutions for this problem are hard to find. The goal of this paper is to devise an efficient algorithm for determining the capacity region of Gaussian MIMO BCs subject to PAPC, which is scalable to the problem size. To this end, we first transform the weighted sum capacity maximization problem, which is inherently nonconvex with the input covariance matrices, into a convex formulation in the dual multiple access channel by minimax duality. Then we derive a computationally efficient algorithm combining the concept of alternating optimization and successive convex approximation. The proposed algorithm achieves much lower complexity compared to an existing interiorpoint method. Moreover, numerical results demonstrate that the proposed algorithm converges very fast under various scenarios.

Connected through hyperedge HE-1 (induced by source paper "on estimating maximum sum rate of mimo systems with successive zero forcing dirty paper coding and per antenna power constraint"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "low complexity approaches for mimo capacity with per antenna power constraint"
    - "miso capacity with per antenna power constraint"

Connected through hyperedge HE-2 (induced by source paper "rate splitting multiple access for coordinated multi point joint transmission"):
  with members:
    - "energy efficiency of rate splitting multiple access and performance benefits over sdma and noma"
    - "rate splitting for multi antenna non orthogonal unicast and multicast transmission"
    - "rate splitting multiple access for downlink communication systems bridging generalizing and outperforming sdma and noma"
    - "sum rate maximization for linearly precoded downlink multiuser miso systems with partial csit a rate splitting approach"
    - "achievable dof regions of mimo networks with imperfect csit"
    - "miso networks with imperfect csit a topological rate splitting approach"
    - "a rate splitting strategy for massive mimo with imperfect csit"
    - "rate analysis of two receiver miso broadcast channel with finite rate feedback a rate splitting approach"

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)