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 survey of biological building blocks for synthetic molecular communication systems"
  Abstract: The design and implementation of synthetic molecular communication systems at nano- and microscale are very challenging. This is particularly true for synthetic MCSs employing biological components as transmitters and receivers or as interfaces with natural biological MCSs. Nevertheless, since such biological components have been optimized by nature over billions of years, using them in synthetic MCSs is highly promising. This paper provides a survey of biological components that can potentially serve as the main building blocks, i.e., transmitter, receiver, and signaling particles, for the design and implementation of synthetic MCSs. Nature uses a large variety of signaling particles of different sizes and with vastly different properties for communication among biological entities. Here, we focus on three important classes of signaling particles: cations, neurotransmitters, and phosphopeptides. These three classes have unique and distinct features such as their large diffusion coefficients, their specificity, and/or their uniqueness of signaling that make them suitable candidates for signaling particles in synthetic MCSs. For each of these candidate signaling particles, we present several specific transmitter and receiver structures mainly built upon proteins that are capable of performing the distinct physiological functionalities required from the transmitters and receivers of MCSs. One of the main advantages of employing proteins for signal emission and detection is that they can be modified with tools from synthetic biology and be tailored to a wide range of application needs. We discuss the properties and limitations of the resulting synthetic MCSs in detail. Furthermore, we outline the possible challenges arising in the implementation of the proposed transmitter and receiver architectures and options for intersymbol interference mitigation for the considered signaling particles.

Connected through hyperedge HE-1 (induced by source paper "diffusive molecular communications with reactive molecules channel modeling and signal design"):
  with members:
    - "channel modeling for diffusive molecular communication a tutorial review"
    - "a novel experimental platform for in vessel multi chemical molecular communications"
    - "diffusive molecular communications with reactive signaling"
    - "channel estimation for diffusive molecular communications"
    - "a comprehensive survey of recent advancements in molecular communication"
    - "improving receiver performance of diffusive molecular communication with enzymes"
    - "transmit pulse shaping for molecular communications"
    - "isi mitigation techniques in molecular communication"

Connected through hyperedge HE-2 (induced by source paper "channel modeling for diffusive molecular communication a tutorial review"):
  with members:
    - "stochastic channel modeling for diffusive mobile molecular communication systems"
    - "a novel experimental platform for in vessel multi chemical molecular communications"
    - "generalized solution for the demodulation of reaction shift keying signals in molecular communication networks"
    - "diffusive mobile molecular communications over time variant channels"
    - "symbol synchronization for diffusion based molecular communications"
    - "effective enzyme deployment for degradation of interference molecules in molecular communication"
    - "modeling duct flow for molecular communication"
    - "molecular communication using magnetic nanoparticles"

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)