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: "transmitter and receiver architectures for molecular communications a survey on physical design with modulation coding and detection techniques"
  Abstract: Inspired by nature, molecular communications (MC), i.e., the use of molecules to encode, transmit, and receive information, stands as the most promising communication paradigm to realize the nanonetworks. Even though there has been extensive theoretical research toward nanoscale MC, there are no examples of implemented nanoscale MC networks. The main reason for this lies in the peculiarities of nanoscale physics, challenges in nanoscale fabrication, and highly stochastic nature of the biochemical domain of envisioned nanonetwork applications. This mandates developing novel device architectures and communication methods compatible with MC constraints. To that end, various transmitter and receiver designs for MC have been proposed in the literature together with numerable modulation, coding, and detection techniques. However, these works fall into domains of a very wide spectrum of disciplines, including, but not limited to, information and communication theory, quantum physics, materials science, nanofabrication, physiology, and synthetic biology. Therefore, we believe it is imperative for the progress of the field that an organized exposition of cumulative knowledge on the subject matter can be compiled. Thus, to fill this gap, in this comprehensive survey, we review the existing literature on transmitter and receiver architectures toward realizing MC among nanomaterial-based nanomachines and/or biological entities and provide a complete overview of modulation, coding, and detection techniques employed for MC. Moreover, we identify the most significant shortcomings and challenges in all these research areas and propose potential solutions to overcome some of them.

Connected through hyperedge HE-1 (induced by source paper "a survey of biological building blocks for synthetic molecular communication systems"):
  with members:
    - "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"
    - "applications of molecular communications to medicine a survey"

Connected through hyperedge HE-2 (induced by source paper "channel sensing in molecular communications with single type of ligand receptors"):
  with members:
    - "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"
    - "modeling and analysis of sinw fet based molecular communication receiver"
    - "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 "molecular information delivery in porous media"):
  with members:
    - "arrival modeling and error analysis for molecular communication via diffusion with drift"

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)