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: "interference steering to manage interference"
  Abstract: To enable densely deployed base stations (BSs) or access points (APs) to serve an increasing number of users and provide diverse mobile services, we need to improve spectrum utilization in wireless communication networks. Although spectral efficiency (SE) can be enhanced via smart and dynamic resource allocation, interference has become a major impediment in improving SE. There have been numerous interference management (IM) proposals at the interfering transmitter or the victim transmitter/receiver separately or cooperatively. Moreover, the existing IM schemes rely mainly on the use of channel state information (CSI). However, in some communication scenarios, the option to adjust the interferer is not available, and, in the case of downlink transmission, it is always difficult or even impossible for the victim receiver to acquire necessary information for IM. Based on the above observations, we first propose a novel IM technique, called interference steering (IS). By making use of both CSI w.r.t. and data carried in the interfering signal, IS generates a signal to modify the spatial feature of the original interference, so that the steered interference at the victim receiver is orthogonal to its intended signal. We then apply IS to an infrastructurebased enterprise wireless local area network (WLAN) in which the same frequency band is reused by adjacent basic service sets (BSSs) with overlapping areas. With IS, multiple nearby APs could simultaneously transmit data on the same channel to their mobile stations (STAs), thus enhancing spectrum reuse. Our in-depth simulation results show that IS significantly improves network SE over existing IM schemes.

Connected through hyperedge HE-1 (induced by source paper "dynamic interference steering in heterogeneous cellular networks"):
  with members:
    - "instantaneous relaying optimal strategies and interference neutralization"
    - "on feasibility of interference alignment in mimo interference networks"
    - "downlink interference alignment"
    - "on feasibility of interference alignment in mimo interference networks"
    - "transmission techniques for relay interference 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)