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: "throughput analysis of cognitive wireless networks with poisson distributed nodes based on location information"
  Abstract: This paper provides a statistical characterization of the individual achievable rates in bits/s/Hz and the spatial throughput of bipolar Poisson wireless networks in bits/s/Hz/m$^2$. We assume that all transmitters have a cognitive ability to know the distance to their receiver's closest interferers so they can individually tune their coding rates to avoid outage events for each spatial realization. Considering that the closest interferer approximates the aggregate interference of all transmitters treated as noise, we derive closed-form expressions for the probability density function of the achievable rates under two decoding rules: treating interference as noise, and jointly detecting the strongest interfering signals treating the others as noise. Based on these rules and the bipolar model, we approximate the expected maximum spatial throughput, showing the best performance of the latter decoding rule. These results are also compared to the reference scenario where the transmitters do not have cognitive ability, coding their messages at predetermined rates that are chosen to optimize the expected spatial throughput -- regardless of particular realizations -- which yields outages. We prove that, when the same decoding rule and network density are considered, the cognitive spatial throughput always outperforms the other option.

Connected through hyperedge HE-1 (induced by source paper "energy efficiency of an unlicensed wireless network in the presence of retransmissions"):
  with members:
    - "increasing the throughput of an unlicensed wireless network through retransmissions"
    - "maximizing the link throughput between smart meters and aggregators as secondary users under power and outage constraints"
    - "on the joint impact of beamwidth and orientation error on throughput in directional wireless poisson networks"
    - "green cellular networks a survey some research issues and challenges"
    - "an overview of the transmission capacity of wireless networks"

Connected through hyperedge HE-2 (induced by source paper "increasing the throughput of an unlicensed wireless network through retransmissions"):
  with members:
    - "maximizing the link throughput between smart meters and aggregators as secondary users under power and outage constraints"
    - "on the joint impact of beamwidth and orientation error on throughput in directional wireless poisson networks"
    - "interference networks with point to point codes"
    - "an overview of the transmission capacity of wireless networks"

Connected through hyperedge HE-3 (induced by source paper "maximizing the link throughput between smart meters and aggregators as secondary users under power and outage constraints"):
  with members:
    - "enhanced transmit antenna selection scheme for secure throughput maximization without csi at the transmitter and its applications on smart grids"
    - "what can wireless cellular technologies do about the upcoming smart metering traffic"
    - "on the joint impact of beamwidth and orientation error on throughput in directional wireless poisson networks"
    - "models for the modern power grid"
    - "machine learning in wireless sensor networks algorithms strategies and applications"
    - "an overview of the transmission capacity of wireless networks"
    - "the effect of fading channel inversion and threshold scheduling on ad hoc networks"

Connected through hyperedge HE-4 (induced by source paper "average error probability in wireless sensor networks with imperfect sensing and communication for different decision rules"):
  with members:
    - "robust decentralized detection and social learning in tandem networks"
    - "effects of relay selection strategies on the spectral efficiency of wireless systems with half and full duplex nodes"
    - "a survey on internet of things from industrial market perspective"
    - "detection performance in balanced binary relay trees with node and link failures"
    - "error probability bounds for balanced binary relay trees"

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)