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: "modeling and analysis of sinw fet based molecular communication receiver"
  Abstract: Molecular communication (MC) is a bio-inspired communication method based on the exchange of molecules for information transfer among nanoscale devices. MC has been extensively studied from various aspects in the literature; however, the physical design of MC transceiving units is largely neglected with the assumption that network nodes are entirely biological devices, e.g., engineered bacteria, which are intrinsically capable of receiving and transmitting molecular messages. However, the low information processing capacity of biological devices and the challenge to interface them with macroscale networks hinder the true application potential of nanonetworks. To overcome this limitation, recently, we proposed a nanobioelectronic MC receiver architecture exploiting the nanoscale field-effect transistor-based biosensor (bioFET) technology, which provides noninvasive and sensitive molecular detection while producing electrical signals as the output. In this paper, we introduce a comprehensive model for silicon nanowire FET-based MC receivers by integrating the underlying processes in MC and bioFET to provide a unified analysis framework. We derive closed-form expressions for the noise statistics, the signal-to-noise ratio (SNR) at the receiver output, and the symbol error probability (SEP). Performance evaluation in terms of SNR and SEP reveals the effects of individual system parameters on the detection performance of the proposed MC receiver.

Connected through hyperedge HE-1 (induced by source paper "transmitter and receiver architectures for molecular communications a survey on physical design with modulation coding and detection techniques"):
  with members:
    - "magnetic nanoparticle based molecular communication in microfluidic environments"
    - "diffusion based molecular communication with limited molecule production rate"
    - "stochastic channel modeling for diffusive mobile molecular communication systems"
    - "exploiting diversity in molecular timing channels via order statistics"
    - "experimental molecular communication testbed based on magnetic nanoparticles in duct flow"
    - "diversity gain of one shot communication over molecular timing channels"
    - "a novel experimental platform for in vessel multi chemical molecular communications"
    - "on the design of matched filters for molecule counting receivers"

Connected through hyperedge HE-2 (induced by source paper "channel sensing in molecular communications with single type of ligand receptors"):
  with members:
    - "transmitter and receiver architectures for molecular communications a survey on physical design with modulation coding and detection techniques"
    - "on the physical design of molecular communication receiver based on nanoscale biosensors"
    - "modeling and simulation of molecular communication systems with a reversible adsorption receiver"
    - "comprehensive reactive receiver modeling for diffusive molecular communication systems reversible binding molecule degradation and finite number of receptors"
    - "channel estimation for diffusive molecular communications"
    - "joint channel parameter estimation via diffusive molecular communication"
    - "a markovian approach to the optimal demodulation of diffusion based molecular communication networks"
    - "novel modulation techniques using isomers as messenger molecules for nano communication networks via diffusion"

Connected through hyperedge HE-3 (induced by source paper "on the capacity of diffusion based molecular communications with sinw fet based receiver"):
  with members:
    - "on the physical design of molecular communication receiver based on nanoscale biosensors"

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.ET(Emerging Technologies)