human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "channel estimation for diffusive molecular communications"):
  Abstract: In molecular communication (MC) systems, the  expected  number of molecules observed at the receiver over time after the instantaneous release of molecules by the transmitter is referred to as the channel impulse response (CIR). Knowledge of the CIR is needed for the design of detection and equalization schemes. In this paper, we present a training-based CIR estimation framework for MC systems, which aims at estimating the CIR based on the  observed  number of molecules at the receiver due to emission of a  sequence  of known numbers of molecules by the transmitter. Thereby, we distinguish two scenarios depending on whether or not statistical channel knowledge is available. In particular, we derive maximum likelihood and least sum of square errors estimators, which do not require any knowledge of the channel statistics. For the case, when statistical channel knowledge is available, the corresponding maximum  a posteriori  and linear minimum mean square error estimators are provided. As performance bound, we derive the classical Cramer Rao (CR) lower bound, valid for any unbiased estimator, which does not exploit statistical channel knowledge, and the Bayesian CR lower bound, valid for any unbiased estimator, which exploits statistical channel knowledge. Finally, we propose the optimal and suboptimal training sequence designs for the considered MC system. Simulation results confirm the analysis and compare the performance of the proposed estimation techniques with the respective CR lower bounds.

Includes members:
  Member "reactive receiver modeling for diffusive molecular communication systems with molecule degradation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "analyzing large scale multiuser molecular communication via 3d stochastic geometry")
  Member "channel estimation techniques for diffusion based molecular communications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "active versus passive receiver model transforms for diffusive molecular communication")
  Member "a comprehensive survey of recent advancements in molecular communication":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "transmitter and receiver architectures for molecular communications a survey on physical design with modulation coding and detection techniques")
  Member "amplify and forward relaying in two hop diffusion based molecular communication networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "symbol by symbol maximum likelihood detection for cooperative molecular communication")
  Member "bounds on distance estimation via diffusive molecular communication":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "channel estimation techniques for diffusion based molecular communications")
  Member "effect of receptor density and size on signal reception in molecular communication via diffusion with an absorbing receiver":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "channel modeling for diffusive molecular communication a tutorial review")
  Member "a unifying model for external noise sources and isi in diffusive molecular communication":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "transmitter and receiver architectures for molecular communications a survey on physical design with modulation coding and detection techniques")
  Member "optimal receiver design for diffusive molecular communication with flow and additive noise":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "transmitter and receiver architectures for molecular communications a survey on physical design with modulation coding and detection techniques")

Question: Please tell me which class the source paper 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)