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 "symbol by symbol 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 (TX) symbol that is more likely, given the likelihood of its observations from multiple receivers (RXs), where the TX sends a common information symbol to all RXs. The transmission of a sequence of binary symbols and the resultant intersymbol interference are considered in the cooperative MC system. We propose five ML detection variants according to different constraints on the knowledge at the FC. These five variants demonstrate trade-offs between their performance and the information available. The system error probabilities for three ML detector variants are derived, two of which are 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 these simpler cooperative variants have error performance comparable to ML detectors when the reporting from RXs to the FC is noisy.

Includes members:
  Member "maximum likelihood detection for cooperative molecular communication":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "design and performance analysis of dual and multi hop diffusive molecular communication systems")
  Member "cooperative abnormality detection via diffusive molecular communications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "using spatial partitioning to reduce the bit error rate of diffusion based molecular communications")
  Member "abnormality detection in correlated gaussian molecular nano networks design and analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "abnormality detection inside blood vessels with mobile nanomachines")
  Member "distributed cooperative detection for multi receiver molecular communication":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "maximum likelihood detection for cooperative molecular communication")
  Member "molecular mimo from theory to prototype":
    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 "a comprehensive survey of recent advancements in molecular communication":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "transmitter and receiver architectures for molecular communications a survey on physical design with modulation coding and detection techniques")
  Member "analysis and design of multi hop diffusion based molecular communication networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "high dimensional metric combining for non coherent molecular signal detection")

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)