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: "maximum likelihood detection for cooperative molecular communication"
  Abstract: In this paper, symbol-by-symbol maximum likelihood (ML) detection is proposed for a cooperative diffusion-based molecular communication (MC) system. In this system, a fusion center (FC) chooses the transmitter's symbol that is more likely, given the likelihood of the observations from multiple receivers (RXs). We propose three different ML detection variants according to different constraints on the information available to the FC, which enables us to demonstrate trade- offs in their performance versus the information available. The system error probability for one variant is derived in closed form. Numerical and simulation results show that the ML detection variants provide lower bounds on the error performance of the simpler cooperative variants and demonstrate that majority rule detection has performance comparable to ML detection when the reporting is noisy.

Connected through hyperedge HE-1 (induced by source paper "symbol by symbol maximum likelihood detection for cooperative molecular communication"):
  with members:
    - "cooperative abnormality detection via diffusive molecular communications"
    - "convex optimization of distributed cooperative detection in multi receiver molecular communication"
    - "abnormality detection in correlated gaussian molecular nano networks design and analysis"
    - "distributed cooperative detection for multi receiver molecular communication"
    - "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"
    - "joint channel parameter estimation via diffusive molecular communication"

Connected through hyperedge HE-2 (induced by source paper "design and performance analysis of dual and multi hop diffusive molecular communication systems"):
  with members:
    - "scw codes for maximum likelihood detection in diffusive molecular communications without channel state information"
    - "non coherent detection for diffusive molecular communications"
    - "channel estimation for diffusive molecular communications"
    - "analysis and design of multi hop diffusion based molecular communication networks"
    - "arrival modeling and error analysis for molecular communication via diffusion with drift"
    - "improving receiver performance of diffusive molecular communication with enzymes"
    - "symbol interval optimization for molecular communication with drift"
    - "molecular communication between two populations of bacteria"

Connected through hyperedge HE-3 (induced by source paper "distributed cooperative detection for multi receiver molecular communication"):
  with members:
    - "convex optimization of distributed cooperative detection in multi receiver molecular communication"
    - "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"
    - "optimal receiver design for diffusive molecular communication with flow and additive noise"
    - "using dimensional analysis to assess scalability and accuracy in molecular communication"
    - "design and analysis of wireless communication systems using diffusion based molecular communication among bacteria"

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)