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: "sidelobe control in collaborative beamforming via node selection"
  Abstract: Collaborative beamforming (CB) is a power efficient method for data communications in wireless sensor networks (WSNs) which aims at increasing the transmission range in the network by radiating the power from a cluster of sensor nodes in the directions of the intended base stations or access points (BSs/APs). The CB average beampattern shows a deterministic behavior and the mainlobe of the CB sample beampattern is independent of the particular node locations. However, the CB for a cluster of a finite number of collaborative nodes results in a sample beampattern with sidelobes that severely depend on the particular node locations. High level sidelobes can cause unacceptable interference when they occur at directions of unintended BSs/APs. Therefore, sidelobe control in CB has a potential to decrease the interference at unintended BSs/APs and increase the network transmission rate by enabling simultaneous multilink CB. Traditional sidelobe control techniques are proposed for centralized antenna arrays and are not suitable for WSNs. In this paper, we show that scalable and low-complexity sidelobe control techniques suitable for CB in WSNs can be developed based on a node selection technique which makes use of the randomness of the node locations. A node selection algorithm with low-rate feedback is developed to search over different node combinations. The performance of the proposed algorithm is analyzed in terms of the average number of search trials required for selecting the collaborative nodes, the resulting interference, and the corresponding transmission rate improvements. Our simulation results show that the interference can be significantly reduced and the transmission rate can be significantly increased when node selection is implemented with CB. The simulation results also show close agreement with our theoretical results.

Connected through hyperedge HE-1 (induced by source paper "simple semi distributed lifetime maximizing strategy via power allocation in collaborative beamforming for wireless sensor networks"):
  with members:
    - "gossip algorithms for distributed signal processing"
    - "collaborative beamforming for distributed wireless ad hoc sensor networks"

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)