human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "cooperative abnormality detection via diffusive molecular communications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "abnormality detection inside blood vessels with mobile nanomachines")
  Member "convex optimization of distributed cooperative detection in multi receiver molecular communication":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "using spatial partitioning to reduce the bit error rate of diffusion based molecular communications")
  Member "distributed cooperative detection for multi receiver molecular communication":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "symbol by symbol maximum likelihood detection for cooperative molecular communication")
  Member "molecular mimo from theory to prototype":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "transmitter and receiver architectures for molecular communications a survey on physical design with modulation coding and detection techniques")
  Member "molecular communications channel model and physical layer techniques":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey of biological building blocks for synthetic molecular communication systems")
  Member "optimal receiver design for diffusive molecular communication with flow and additive noise":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "transmitter and receiver architectures for molecular communications a survey on physical design with modulation coding and detection techniques")
  Member "using dimensional analysis to assess scalability and accuracy in molecular communication":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "channel modeling for diffusive molecular communication a tutorial review")

Question: Please tell me which class the source paper 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)