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: "improving reliability performance of diffusion based molecular communication with adaptive threshold variation algorithm"
  Abstract: In this work, we investigate the communication reliability for diffusion-based molecular communication, using the indicator of bit error rate (BER). A molecular classified model is established to divide molecules into three parts, which are the signal, inter-symbol interference (ISI) and noise. We expand each part separately using molecular absorbing probability, and connect them by a traditional-like formula. Based on the classified model, we do a theoretical analysis to prove the feasibility of improving the BER performance. Accordingly, an adaptive threshold variation (ATV) algorithm is designed in demodulation to implement the goal, which makes the receiver adapt the channel condition properly through learning process. Moreover, the complexity of ATV is calculated and its performance in various noisy channel is discussed. An expression of Signal to Interference plus Noise Ratio (SINR) is defined to verify the system performance. We test some important parameters of the channel model, as well as the ATV algorithm in the simulation section. The results have shown the performance gain of the proposal.

Connected through hyperedge HE-1 (induced by source paper "transmitter and receiver architectures for molecular communications a survey on physical design with modulation coding and detection techniques"):
  with members:
    - "magnetic nanoparticle based molecular communication in microfluidic environments"
    - "diffusion based molecular communication with limited molecule production rate"
    - "stochastic channel modeling for diffusive mobile molecular communication systems"
    - "exploiting diversity in molecular timing channels via order statistics"
    - "experimental molecular communication testbed based on magnetic nanoparticles in duct flow"
    - "diversity gain of one shot communication over molecular timing channels"
    - "a novel experimental platform for in vessel multi chemical molecular communications"
    - "on the design of matched filters for molecule counting receivers"

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)