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 for diffusive mimo molecular communications"
  Abstract: In diffusion-based communication, as for molecular systems, the achievable data rate is very low due to the slow nature of diffusion and the existence of severe inter-symbol-interference (ISI). Multiple-input multiple-output (MIMO) technique can be used to improve the data rate. Knowledge of channel impulse response (CIR) is essential for equalization and detection in MIMO systems. This paper presents a training-based CIR estimation for diffusive MIMO (D-MIMO) channels. Maximum likelihood and least-squares estimators are derived, and the training sequences are designed to minimize the corresponding Cramer-Rao bound. Sub-optimal estimators are compared to Cramer-Rao bound to validate their performance.

Connected through hyperedge HE-1 (induced by source paper "index modulation for molecular communication via diffusion systems"):
  with members:
    - "machine learning based channel modeling for molecular mimo communications"
    - "spatial diversity in molecular communications"
    - "modeling and simulation of molecular communication systems with a reversible adsorption receiver"
    - "index modulation techniques for 5g wireless networks"
    - "molecular mimo from theory to prototype"
    - "a comprehensive survey of recent advancements in molecular communication"
    - "molecular mimo communication link"
    - "detection algorithms for molecular mimo"

Connected through hyperedge HE-2 (induced by source paper "spatial modulation for molecular communication"):
  with members:
    - "spatial coding techniques for molecular mimo"
    - "machine learning based channel modeling for molecular mimo communications"
    - "active versus passive receiver model transforms for diffusive molecular communication"
    - "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-3 (induced by source paper "diffusive mimo molecular communications channel estimation equalization and detection"):
  with members:
    - "stochastic channel modeling for diffusive mobile molecular communication systems"
    - "on the capacity of diffusion based molecular timing channels"
    - "comprehensive reactive receiver modeling for diffusive molecular communication systems reversible binding molecule degradation and finite number of receptors"
    - "channel estimation for diffusive molecular communications"
    - "molecular mimo from theory to prototype"
    - "a comprehensive survey of recent advancements in molecular communication"
    - "analysis and design of multi hop diffusion based molecular communication networks"
    - "improving receiver performance of diffusive molecular communication with enzymes"

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)