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: "optimal demodulation of reaction shift keying signals in diffusion based molecular communication networks"
  Abstract: In a diffusion-based molecular communication network, transmitters and receivers communicate by using signalling molecules (or ligands) in a fluid medium. This paper proposes a novel modulation mechanism for molecular communication called Reaction Shift Keying (RSK). In RSK, the transmitter uses different chemical reactions to generate different time-varying functions of concentration of signalling molecules to represent different transmission symbols. We consider the problem of demodulating the RSK symbols assuming that the transmitter and receiver are synchronised. We assume the receiver consists of receptors and signalling molecules may react with these receptors to form ligand-receptor complexes. We derive an optimal RSK demodulator using the continuous history of the number of complexes at the receiver as the input to the demodulator. We do that by first deriving a communication model which includes the chemical reactions in the transmitter, diffusion in the transmission medium and the ligand-receptor process in the receiver. This model, which takes the form of a continuous-time Markov process, captures the noise in the receiver signal due to the stochastic nature of chemical reactions and diffusion. We then adopt a maximum posterior framework and use Bayesian filtering to derive the optimal demodulator for RSK signals. We use numerical examples to illustrate the properties of the RSK demodulator.

Connected through hyperedge HE-1 (induced by source paper "modeling and simulation of molecular communication systems with a reversible adsorption receiver"):
  with members:
    - "3d stochastic geometry model for large scale molecular communication systems"
    - "a comprehensive survey of recent advancements in molecular communication"
    - "joint channel parameter estimation via diffusive molecular communication"
    - "a markovian approach to the optimal demodulation of diffusion based molecular communication networks"
    - "molecular communication with a reversible adsorption receiver"
    - "arrival modeling and error analysis for molecular communication via diffusion with drift"
    - "variance constrained capacity of the molecular timing channel with synchronization error"
    - "improving receiver performance of diffusive molecular communication with enzymes"

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)