human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "harvesting full duplex rate gains in cellular networks with half duplex user terminals"):
  Abstract: Full-Duplex (FD) transceivers may be expensive in terms of complexity, power consumption, and price to be implemented in all user terminals. Therefore, techniques to exploit in-band full-duplex communication with FD base stations (BSs) and half-duplex (HD) users' equipment (UEs) are required. In this context, 3-node topology (3NT) has been recently proposed for FD BSs to reuse the uplink (UL) and downlink (DL) channels with HD terminals within the same cell. In this paper, we present a tractable mathematical framework, based on stochastic geometry, for 3NT in cellular networks. To this end, we propose a design paradigm via pulse-shaping and partial overlap between UL and DL channels to maximize the harvested rate gains in 3NT. The results show that 3NT achieves a close performance to networks with FD BSs and FD UEs, denoted by 2-node topology (2NT) networks. A maximum of 5% rate loss is reported when 3NT is compared to 2NT with efficient self-interference cancellation (SIC). If the SIC in 2NT is not efficient, 3NT highly outperforms 2NT. Consequently, we conclude that, irrespective to the UE duplexing scheme, it is sufficient to have FD BSs to harvest FD rate gains.

Includes members:
  Member "in band full duplex communications for cellular networks with partial uplink downlink overlap":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "area spectral efficiency and coverage for mixed duplexing networks with directional transmissions")
  Member "limits on the capacity of in band full duplex communication in uplink cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "modeling cellular networks with full duplex d2d communication a stochastic geometry approach")
  Member "full duplex radio for uplink downlink transmission with spatial randomness":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "directional antennas for interference management in full duplex cellular networks")
  Member "interference management with partial uplink downlink spectrum overlap":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "flexible design for alpha duplex communications in multi tier cellular networks")
  Member "in band alpha duplex scheme for cellular networks a stochastic geometry approach":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "performance of network assisted full duplex for cell free massive mimo")
  Member "outage analysis of full duplex architectures in cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "directional antennas for interference management in full duplex cellular networks")
  Member "on stochastic geometry modeling of cellular uplink transmission with truncated channel inversion power control":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a spatiotemporal framework for information freshness in iot uplink networks")
  Member "throughput analysis for wireless networks with full duplex radios":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "performance analysis of joint pairing and mode selection in d2d communications with fd radios")

Question: Please tell me which class the source paper 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)