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: "generalized solution for the demodulation of reaction shift keying signals in molecular communication networks"
  Abstract: This paper considers a diffusion-based molecular communication system, where the transmitter uses reaction shift keying (RSK) as the modulation scheme. We focus on the demodulation of RSK signal at the receiver. The receiver consists of a front-end molecular circuit and a back-end demodulator. The front-end molecular circuit is a set of chemical reactions consisting of multiple chemical species. The optimal demodulator computes the posteriori probability of the transmitted symbols given the history of the observation. The derivation of the optimal demodulator requires the solution to a specific Bayesian filtering problem. The solution to this Bayesian filtering problem had been derived for a few specific molecular circuits and specific choice(s) of observed chemical species. The derivation of such solution is also lengthy. The key contribution of this paper is to present a general solution to this Bayesian filtering problem, which can be applied to any molecular circuit and any choice of observed species.

Connected through hyperedge HE-1 (induced by source paper "using spatial partitioning to reduce the bit error rate of diffusion based molecular communications"):
  with members:
    - "designing molecular circuits for approximate maximum a posteriori demodulation of concentration modulated signals"
    - "on the design of matched filters for molecule counting receivers"
    - "improving the capacity of molecular communication using enzymatic reaction cycles"
    - "on medium chemical reaction in diffusion based molecular communication a two way relaying example"
    - "convex optimization of distributed cooperative detection in multi receiver molecular communication"
    - "a comprehensive survey of recent advancements in molecular communication"
    - "a markovian approach to the optimal demodulation of diffusion based molecular communication networks"
    - "improving receiver performance of diffusive molecular communication with enzymes"

Connected through hyperedge HE-2 (induced by source paper "designing molecular circuits for approximate maximum a posteriori demodulation of concentration modulated signals"):
  with members:
    - "a comprehensive survey of recent advancements in molecular communication"
    - "a markovian approach to the optimal demodulation of diffusion based molecular communication networks"
    - "optimal receiver design for diffusive molecular communication with flow and additive noise"
    - "extended master equation models for molecular communication networks"

Connected through hyperedge HE-3 (induced by source paper "channel modeling for diffusive molecular communication a tutorial review"):
  with members:
    - "a survey of biological building blocks for synthetic molecular communication systems"
    - "stochastic channel modeling for diffusive mobile molecular communication systems"
    - "a novel experimental platform for in vessel multi chemical molecular communications"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "improving the capacity of molecular communication using enzymatic reaction cycles"):
  with members:
    - "effective enzyme deployment for degradation of interference molecules in molecular communication"
    - "a comprehensive survey of recent advancements in molecular communication"
    - "a markovian approach to the optimal demodulation of diffusion based molecular communication networks"
    - "improving receiver performance of diffusive molecular communication with enzymes"
    - "optimal receiver design for diffusive molecular communication with flow and additive noise"
    - "extended master equation models for molecular communication networks"
    - "capacity of diffusion based molecular communication with ligand receptors"
    - "capacity of discrete molecular diffusion channels"

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)