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: "simple semi distributed lifetime maximizing strategy via power allocation in collaborative beamforming for wireless sensor networks"
  Abstract: Energy-efficient communication is an important issue in wireless sensor networks (WSNs) consisting of large number of energy-constrained sensor nodes. Indeed, sensor nodes have different energy budgets assigned to data transmission at individual nodes. Therefore, without energy-aware transmission schemes, energy can deplete from sensor nodes with smaller energy budget faster than from the rest of the sensor nodes in WSNs. This reduces the coverage area as well as the lifetime of WSNs. Collaborative beamforming (CB) has been proposed originally to achieve directional gain, however, it also inherently distributes the corresponding energy consumption over the collaborative sensor nodes. In fact, CB can be seen as a physical layer solution (versus the media access control/network layer solution) to balance the lifetimes of individual sensor nodes and extend the lifetime of the whole WSN. However, the introduction of energy-aware CB schemes is critical for extending the WSNs lifetime. #R##N#In this paper, CB with power allocation (CB-PA) is developed to extend the lifetime of a cluster of collaborative sensor nodes by balancing the individual sensor node lifetimes. A novel strategy is proposed to utilize the residual energy information available at each sensor node. It adjusts the energy consumption rate at each sensor node while achieving the required average signal-to-noise ratio (SNR) at the destination. It is a semi-distributed strategy and it maintains average SNR. Different factors affecting the energy consumption are studied as well. Simulation results show that CB-PA outperforms CB with Equal Power Allocation (CB-EPA) in terms of extending the lifetime of a cluster of collaborative nodes.

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)