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: "channel estimation techniques for diffusion based 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. In particular, we derive maximum likelihood (ML) and least sum of square errors (LSSE) estimators. We also study the Cramer Rao (CR) lower bound and training sequence design for the considered system. Simulation results confirm the analysis and compare the performance of the proposed estimation techniques with the CR lower bound.

Connected through hyperedge HE-1 (induced by source paper "channel estimation for diffusive molecular communications"):
  with members:
    - "reactive receiver modeling for diffusive molecular communication systems with molecule degradation"
    - "a comprehensive survey of recent advancements in molecular communication"
    - "amplify and forward relaying in two hop diffusion based molecular communication networks"
    - "bounds on distance estimation via diffusive molecular communication"
    - "effect of receptor density and size on signal reception in molecular communication via diffusion with an absorbing receiver"
    - "a unifying model for external noise sources and isi in diffusive molecular communication"
    - "optimal receiver design for diffusive molecular communication with flow and additive noise"
    - "channel and noise models for nonlinear molecular communication systems"

Connected through hyperedge HE-2 (induced by source paper "active versus passive receiver model transforms for diffusive molecular communication"):
  with members:
    - "a comprehensive survey of recent advancements in molecular communication"
    - "optimal receiver design for diffusive molecular communication with flow and additive noise"
    - "effect of degradation in molecular communication impairment or enhancement"
    - "channel and noise models for nonlinear molecular communication systems"
    - "using dimensional analysis to assess scalability and accuracy in molecular communication"
    - "tabletop molecular communication text messages through chemical signals"

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)