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: "lower bounds optimization for coordinated linear transmission beamformer design in multicell network downlink"
  Abstract: We consider the coordinated downlink beamforming problem in a cellular network with the base stations (BSs) equipped with multiple antennas, and with each user equipped with a single antenna. The BSs cooperate in sharing their local interference information, and they aim at maximizing the sum rate of the users in the network. A set of new lower bounds (one bound for each BS) of the non-convex sum rate is identifie d. These bounds facilitate the development of a set of algorithms that allow the BSs to update their beams by optimizing their respective lower bounds. We show that when there is a single user per-BS, the lower bound maximization problem can be solved exactly with rank-1 solutions. In this case, the overall sum rate maximization problem can be solved to a KKT point. Numerical results show that the proposed algorithms achieve high system throughput with reduced backhaul information exchange among the BSs. I. I NTRODUCTION

Connected through hyperedge HE-1 (induced by source paper "joint base station clustering and beamformer design for partial coordinated transmission in heterogenous networks"):
  with members:
    - "linear transceiver design for a mimo interfering broadcast channel achieving max min fairness"
    - "linear precoding in cooperative mimo cellular networks with limited coordination clusters"
    - "balancing egoism and altruism on the interference channel the mimo case"
    - "cooperative multi cell block diagonalization with per base station power constraints"
    - "networked mimo with clustered linear precoding"
    - "consistency of the group lasso and multiple kernel learning"

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)