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: "resource allocation and rate gains in practical full duplex systems"
  Abstract: Full-duplex (FD) communication has the potential to substantially increase the throughput in wireless networks. However, the benefits of FD are still not well understood. In this paper, we characterize the FD rate gains in both single-channel and multi-channel use cases. For the single-channel case, we quantify the rate gain as a function of the remaining self-interference (SI) and signal-to-noise ratio values. We also provide a sufficient condition under which the sum of uplink and downlink rates on an FD channel is biconcave in the transmission power levels. Building on these results, we consider the multi-channel case. For that case, we introduce a new realistic model of a compact (e.g., smartphone) FD receiver and demonstrate its accuracy via measurements. We study the problem of jointly allocating power levels to different channels and selecting the frequency of maximum SI suppression, where the objective is to maximize the sum of the rates over uplink and downlink orthogonal frequency division multiplexing channels. We develop a polynomial time algorithm, which is nearly optimal, in practice, under very mild restrictions. To reduce the running time, we develop an efficient nearly optimal algorithm under the high SINR approximation. Finally, we demonstrate via numerical evaluations the capacity gains in different use cases and obtain insights into the impact of the remaining SI and wireless channel states on the performance.

Connected through hyperedge HE-1 (induced by source paper "capacity limits of full duplex cellular network"):
  with members:
    - "fundamental limits of spectrum sharing full duplex multicell networks"
    - "opportunistic scheduling for full duplex uplink downlink networks"
    - "passive self interference suppression for full duplex infrastructure nodes"
    - "in band full duplex wireless challenges and opportunities"
    - "feedback capacity of the gaussian interference channel to within 2 bits"
    - "bilayer low density parity check codes for decode and forward in relay channels"
    - "gaussian interference channel capacity to within one bit"
    - "rate regions for relay broadcast channels"

Connected through hyperedge HE-2 (induced by source paper "hybrid scheduling in heterogeneous half and full duplex wireless networks"):
  with members:
    - "open access full duplex wireless in the orbit testbed"
    - "on the capacity regions of single channel and multi channel full duplex links"
    - "probabilistic medium access control for full duplex networks with half duplex clients"
    - "prototyping real time full duplex radios"
    - "queue based random access algorithms fluid limits and stability issues"
    - "in band full duplex wireless challenges and opportunities"
    - "experiment driven characterization of full duplex wireless systems"
    - "pushing the limits of full duplex design and real time implementation"

Connected through hyperedge HE-3 (induced by source paper "open access full duplex wireless in the orbit testbed"):
  with members:
    - "in band full duplex wireless challenges and opportunities"

Connected through hyperedge HE-4 (induced by source paper "on the capacity regions of single channel and multi channel full duplex links"):
  with members:
    - "joint beamforming optimization and power control for full duplex mimo two way relay channel"
    - "rate gain region and design tradeoffs for full duplex wireless communications"
    - "in band full duplex wireless challenges and opportunities"
    - "distributed full duplex via wireless side channels bounds and protocols"
    - "experiment driven characterization of full duplex wireless systems"

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)