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: "feedback reduction for random beamforming in multiuser mimo broadcast channel"
  Abstract: For the multiuser multiple-input multiple-output (MIMO) downlink channel, the users feedback their channel state information (CSI) to help the base stati on (BS) schedule users and improve the system sum rate. However, this incurs a large aggregate feedback bandwidth which grows linearly with the number of users. In this paper, we propose a novel scheme to reduce the feedback load in a downlink orthogonal space division multiple access (SDMA) system with zero-forcing receivers by allowing the users to dynamically determine the number of feedback bits to use according to multiple decision thresholds. Through theoretical analysis, we show that, while keeping the aggregate feedback load of the entire system constant regardless of the number of users, the proposed scheme almost achieves the optimal asymptotic sum rate scaling with respect to the number of users (also known as the multiuser diversity). Specifically, given the number of thresholds, t he proposed scheme can achieve a constant portion of the optimal sum rate achievable only by the system where all the users always feedback, and the remaining portion (referred to as the sum rate loss) decr eases exponentially to zero as the number of thresholds increases. By deriving a tight upper bound for the sum rate loss, the minimum number of thresholds for a given tolerable sum rate loss is determined . In addition, a fast bit allocation method is discussed for the proposed scheme, and the simulation results show that the sum rate performances with the complex optimal bit allocation method and with the fast algorithm are almost the same. We compare our multi-threshold scheme to some previously proposed feedback schemes. Through simulation, we demonstrate that the proposed scheme can reduce the feedback load and utilize the limited feedback bandwidth more effectively than the existing feedback methods.

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)