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: "isi aware modeling and achievable rate analysis of the diffusion channel"
  Abstract: Analyzing the achievable rate of molecular communication via diffusion (MCvD) inherits intricacies due to its nature: MCvD channel has memory, and the heavy tail of the signal causes inter-symbol interference (ISI). Therefore, using Shannon’s channel capacity formulation for memoryless channel is not appropriate for the MCvD channel. Instead, a more general achievable rate formulation and system model must be considered to make this analysis accurately. In this letter, we propose an effective ISI-aware MCvD modeling technique in 3-D medium and properly analyze the achievable rate.

Connected through hyperedge HE-1 (induced by source paper "two way molecular communications"):
  with members:
    - "effective enzyme deployment for degradation of interference molecules in molecular communication"
    - "machine learning based channel modeling for molecular mimo communications"
    - "molecular mimo from theory to prototype"
    - "a comprehensive survey of recent advancements in molecular communication"
    - "improving receiver performance of diffusive molecular communication with enzymes"
    - "isi mitigation techniques in molecular communication"
    - "symbol interval optimization for molecular communication with drift"
    - "channel and noise models for nonlinear molecular communication systems"

Connected through hyperedge HE-2 (induced by source paper "molecular signal modeling of a partially counting absorbing spherical receiver"):
  with members:
    - "machine learning based channel modeling for molecular mimo communications"
    - "chemical propagation pattern for molecular communications"
    - "channel impulse responses in diffusive molecular communication with spherical transmitters"
    - "a machine learning approach to model the received signal in molecular communications"
    - "molecular mimo from theory to prototype"
    - "a comprehensive survey of recent advancements in molecular communication"
    - "molecular communications channel model and physical layer techniques"
    - "improving receiver performance of diffusive molecular communication with enzymes"

Connected through hyperedge HE-3 (induced by source paper "spatial coding techniques for molecular mimo"):
  with members:
    - "machine learning based channel modeling for molecular mimo communications"
    - "molecular mimo from theory to prototype"
    - "a comprehensive survey of recent advancements in molecular communication"
    - "molecular mimo communication link"
    - "molecular communications channel model and physical layer techniques"
    - "optimal receiver design for diffusive molecular communication with flow and additive noise"
    - "novel modulation techniques using isomers as messenger molecules for nano communication networks via diffusion"
    - "molecular communication in fluid media the additive inverse gaussian noise channel"

Connected through hyperedge HE-4 (induced by source paper "spatial diversity in molecular communications"):
  with members:
    - "machine learning based channel modeling for molecular mimo communications"
    - "molecular mimo from theory to prototype"
    - "a comprehensive survey of recent advancements in molecular communication"
    - "molecular mimo communication link"
    - "molecular communications channel model and physical layer techniques"
    - "novel modulation techniques using isomers as messenger molecules for nano communication networks via diffusion"
    - "molecular communication in fluid media the additive inverse gaussian noise channel"

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.ET(Emerging Technologies)