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: "active versus passive receiver model transforms for diffusive molecular communication"
  Abstract: This paper presents an analytical comparison of active and passive receiver models in diffusive molecular communication. In the active model, molecules are absorbed when they collide with the receiver surface. In the passive model, the receiver is a virtual boundary that does not affect molecule behavior. Two approaches are presented to derive transforms between the receiver signals. As an example, two models for an unbounded diffusion-only molecular communication system with a spherical receiver are unified. As time increases in the three-dimensional system, the transform functions have constant scaling factors, such that the receiver models are effectively equivalent. Methods are presented to enable the transformation of stochastic simulations, which are used to verify the transforms and demonstrate that transforming the simulation of a passive receiver can be more efficient and more accurate than the direct simulation of an absorbing receiver.

Connected through hyperedge HE-1 (induced by source paper "high dimensional metric combining for non coherent molecular signal detection"):
  with members:
    - "effective enzyme deployment for degradation of interference molecules in molecular communication"
    - "chemical propagation pattern for molecular communications"
    - "low complexity noncoherent signal detection for nanoscale molecular communications"
    - "analysis and design of multi hop diffusion based molecular communication networks"
    - "molecular communications channel model and physical layer techniques"
    - "improving receiver performance of diffusive molecular communication with enzymes"
    - "optimal receiver design for diffusive molecular communication with flow and additive noise"

Connected through hyperedge HE-2 (induced by source paper "end to end performance optimization in hybrid molecular and electromagnetic communications"):
  with members:
    - "applications of molecular communications to medicine a survey"
    - "a comprehensive survey of recent advancements in molecular communication"
    - "analysis and design of multi hop diffusion based molecular communication networks"
    - "relaying in diffusion based molecular communication"
    - "molecular communication in fluid media the additive inverse gaussian noise channel"

Connected through hyperedge HE-3 (induced by source paper "spatial modulation for molecular communication"):
  with members:
    - "spatial coding techniques for molecular mimo"
    - "channel estimation for diffusive mimo molecular communications"
    - "machine learning based channel modeling for molecular mimo communications"
    - "chemical propagation pattern for molecular 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"
    - "massive mimo for next generation wireless systems"

Connected through hyperedge HE-4 (induced by source paper "normal inverse gaussian approximation for arrival time difference in flow induced molecular communications"):
  with members:
    - "a comprehensive survey of recent advancements in molecular communication"
    - "stable distributions as noise models for molecular communication"
    - "symbol interval optimization for molecular communication with drift"
    - "molecular communication in fluid media the additive inverse gaussian noise channel"

Connected through hyperedge HE-5 (induced by source paper "diffusive mobile molecular communications over time variant channels"):
  with members:
    - "comprehensive reactive receiver modeling for diffusive molecular communication systems reversible binding molecule degradation and finite number of receptors"
    - "channel estimation for diffusive molecular communications"

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)