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: "rate splitting for multi user multi antenna wireless information and power transfer"
  Abstract: In a multi-user multi-antenna Simultaneous Wireless Information and Power Transfer (SWIPT) network, the transmitter sends information to the Information Receivers (IRs) and energy to Energy Receivers (ERs) concurrently. A conventional approach is based on Multi-User Linear Precoding (MU--LP) where each IR directly decodes the intended stream by fully treating the interference from other IRs and ERs as noise. In this paper, we investigate the application of linearly-precoded Rate-Splitting (RS) in Multiple Input Single Output (MISO) SWIPT Broadcast Channel (BC). By splitting the messages of IRs into private and common parts and encoding the common parts into a common stream decoded by all IRs, RS manages the interference dynamically. The precoders are designed such that the Weighted Sum Rate (WSR) of IRs is maximized under the total transmit power constraint and the sum energy constraint for ERs. Numerical results show that the proposed RS-assisted strategy provides a better rate-energy tradeoff in MISO SWIPT BC. Under a sum energy constraint of ERs, RS-assisted strategy achieves better WSR performance of IRs than MU--LP and NOMA in a wide range of IR and ER deployments. Hence, we draw the conclusion that RS is superior for downlink SWIPT networks.

Connected through hyperedge HE-1 (induced by source paper "beyond dirty paper coding for multi antenna broadcast channel with partial csit a rate splitting approach"):
  with members:
    - "cooperative rate splitting for miso broadcast channel with user relaying and performance benefits over cooperative noma"
    - "dof region of the miso bc with partial csit proof by inductive fourier motzkin elimination"
    - "rate splitting unifying sdma oma noma and multicasting in miso broadcast channel a simple two user rate analysis"
    - "energy efficiency of rate splitting multiple access and performance benefits over sdma and noma"
    - "rate splitting multiple access for coordinated multi point joint transmission"
    - "rate splitting for multi antenna non orthogonal unicast and multicast transmission"
    - "rate splitting multiple access for downlink communication systems bridging generalizing and outperforming sdma and noma"
    - "rate splitting for multi antenna non orthogonal unicast and multicast transmission spectral and energy efficiency analysis"

Connected through hyperedge HE-2 (induced by source paper "sum rate maximization of uplink rate splitting multiple access rsma communication"):
  with members:
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "interference mitigation via rate splitting and common message decoding in cloud radio access networks"
    - "rate splitting multiple access for downlink communication systems bridging generalizing and outperforming sdma and noma"
    - "mobile unmanned aerial vehicles uavs for energy efficient internet of things communications"
    - "a survey on non orthogonal multiple access for 5g networks research challenges and future trends"
    - "sum rate maximization for linearly precoded downlink multiuser miso systems with partial csit a rate splitting approach"
    - "caching in the sky proactive deployment of cache enabled unmanned aerial vehicles for optimized quality of experience"
    - "robust transmission in downlink multiuser miso systems a rate splitting approach"

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)