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: "a diffusion neuron hybrid system for molecular communication"
  Abstract: Diffusion-based and neural communication are two interesting domains in molecular communication. Both of them have distinct advantages and are exploited separately in many works. However, in some cases, neural and diffusion-based ways have to work together for a communication. Therefore, in this paper, we propose a hybrid communication system, in which the diffusion-based and neural communication channels are contained. Multiple connection nano-devices (CND) are used to connect the two channels. We define the practice function of the CNDs and develop the mechanism of exchanging information from diffusion-based to neural channel, based on the biological characteristics of the both channels. In addition, we establish a brief mathematical model to present the complete communication process of the hybrid system. The information exchange process at the CNDs is shown in the simulation. The bit error rate (BER) indicator is used to verify the reliability of communication. The result reveals that based on the biological channels, optimizing some parameters of nano-devices could improve the reliability performance.

Connected through hyperedge HE-1 (induced by source paper "a survey of biological building blocks for synthetic molecular communication systems"):
  with members:
    - "transmitter and receiver architectures for molecular communications a survey on physical design with modulation coding and detection techniques"
    - "designing molecular circuits for approximate maximum a posteriori demodulation of concentration modulated signals"
    - "index modulation for molecular communication via diffusion systems"
    - "diffusive molecular communications with reactive molecules channel modeling and signal design"
    - "channel modeling for diffusive molecular communication a tutorial review"
    - "a novel experimental platform for in vessel multi chemical molecular communications"
    - "diffusion based molecular communication principle key technologies and challenges"
    - "symbol synchronization for diffusion based molecular communications"

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)