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: "scalable coordinated beamforming for dense wireless cooperative networks"
  Abstract: To meet the ever growing demand for both high throughput and uniform coverage in future wireless networks, dense network deployment will be ubiquitous, for which co- operation among the access points is critical. Considering the computational complexity of designing coordinated beamformers for dense networks, low-complexity and suboptimal precoding strategies are often adopted. However, it is not clear how much performance loss will be caused. To enable optimal coordinated beamforming, in this paper, we propose a framework to design a scalable beamforming algorithm based on the alternative direction method of multipliers (ADMM) method. Specifically, we first propose to apply the matrix stuffing technique to transform the original optimization problem to an equivalent ADMM-compliant problem, which is much more efficient than the widely-used modeling framework CVX. We will then propose to use the ADMM algorithm, a.k.a. the operator splitting method, to solve the transformed ADMM-compliant problem efficiently. In particular, the subproblems of the ADMM algorithm at each iteration can be solved with closed-forms and in parallel. Simulation results show that the proposed techniques can result in significant computational efficiency compared to the state- of-the-art interior-point solvers. Furthermore, the simulation results demonstrate that the optimal coordinated beamforming can significantly improve the system performance compared to sub-optimal zero forcing beamforming.

Connected through hyperedge HE-1 (induced by source paper "low complexity antenna selection for low target rate users in dense cloud radio access networks"):
  with members:
    - "cooperative base station coloring for pair wise multi cell coordination"
    - "large scale convex optimization for dense wireless cooperative networks"
    - "the complexity rate tradeoff of centralized radio access networks"
    - "a stochastic geometry framework for analyzing pairwise cooperative cellular networks"
    - "spectral efficiency of dynamic coordinated beamforming a stochastic geometry approach"
    - "the role of computational outage in dense cloud based centralized radio access networks"
    - "user centric intercell interference nulling for downlink small cell networks"
    - "joint power and antenna selection optimization in large cloud radio access networks"

Connected through hyperedge HE-2 (induced by source paper "robust group sparse beamforming for multicast green cloud ran with imperfect csi"):
  with members:
    - "large scale convex optimization for dense wireless cooperative networks"
    - "large scale convex optimization for ultra dense cloud ran"
    - "what will 5g be"
    - "group sparse beamforming for green cloud ran"
    - "joint precoding and multivariate backhaul compression for the downlink of cloud radio access networks"
    - "optimal stochastic coordinated beamforming for wireless cooperative networks with csi uncertainty"
    - "distributed robust multicell coordinated beamforming with imperfect csi an admm approach"
    - "solving quadratic equations via phaselift when there are about as many equations as unknowns"

Connected through hyperedge HE-3 (induced by source paper "low complexity antenna selection in dense cloud radio access networks"):
  with members:
    - "cooperative base station coloring for pair wise multi cell coordination"
    - "a stochastic geometry framework for analyzing pairwise cooperative cellular networks"
    - "spectral efficiency of dynamic coordinated beamforming a stochastic geometry approach"
    - "the role of computational outage in dense cloud based centralized radio access networks"
    - "user centric intercell interference nulling for downlink small cell networks"
    - "joint power and antenna selection optimization in large cloud radio access networks"
    - "heterogeneous cloud radio access networks a new perspective for enhancing spectral and energy efficiencies"
    - "a tractable model for noncoherent joint transmission base station cooperation"

Connected through hyperedge HE-4 (induced by source paper "large scale convex optimization for dense wireless cooperative networks"):
  with members:
    - "robust group sparse beamforming for multicast green cloud ran with imperfect csi"
    - "large scale convex optimization for ultra dense cloud ran"
    - "optimal multiuser transmit beamforming a difficult problem with a simple solution structure"
    - "base station activation and linear transceiver design for optimal resource management in heterogeneous networks"
    - "group sparse beamforming for green cloud ran"
    - "optimal stochastic coordinated beamforming for wireless cooperative networks with csi uncertainty"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "distributed robust multicell coordinated beamforming with imperfect csi an admm approach"

Connected through hyperedge HE-5 (induced by source paper "large scale convex optimization for ultra dense cloud ran"):
  with members:
    - "large scale convex optimization for dense wireless cooperative networks"
    - "heterogeneous cloud radio access networks a new perspective for enhancing spectral and energy efficiencies"
    - "base station activation and linear transceiver design for optimal resource management in heterogeneous networks"
    - "millimeter wave cellular wireless networks potentials and challenges"
    - "sparse beamforming and user centric clustering for downlink cloud radio access network"
    - "group sparse beamforming for green cloud ran"
    - "optimal stochastic coordinated beamforming for wireless cooperative networks with csi uncertainty"
    - "scaling up mimo opportunities and challenges with very large arrays"

Connected through hyperedge HE-6 (induced by source paper "dynamic nested clustering for parallel phy layer processing in cloud rans"):
  with members:
    - "spectral efficiency of dynamic coordinated beamforming a stochastic geometry approach"
    - "joint power and antenna selection optimization in large cloud radio access networks"
    - "hierarchical interference mitigation for massive mimo cellular networks"
    - "interference coordination random clustering and adaptive limited feedback"
    - "fundamental limits of cooperation"
    - "joint user grouping and linear virtual beamforming complexity algorithms and approximation bounds"
    - "cooperative multi cell block diagonalization with per base station power constraints"
    - "networked mimo with clustered linear precoding"

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)