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 "cooperative abnormality detection via diffusive molecular communications"):
  Abstract: In this paper, we consider abnormality detection via diffusive molecular communications (MCs) for a network consisting of several sensors and a fusion center (FC). If a sensor detects an abnormality, it injects into the medium a number of molecules which is proportional to the sensed value. Two transmission schemes for releasing molecules into the medium are considered. In the first scheme, referred to as DTM, each sensor releases a different type of molecule, whereas in the second scheme, referred to as STM, all sensors release the same type of molecule. The molecules released by the sensors propagate through the MC channel and some may reach the FC where the final decision regarding whether or not an abnormality has occurred is made. We derive the optimal decision rules for both DTM and STM. However, the optimal detectors entail high computational complexity as log-likelihood ratios (LLRs) have to be computed. To overcome this issue, we show that the optimal decision rule for STM can be transformed into an equivalent low-complexity decision rule. Since a similar transformation is not possible for DTM, we propose simple low-complexity sub-optimal detectors based on different approximations of the LLR. The proposed low-complexity detectors are more suitable for practical MC systems than the original complex optimal decision rule, particularly when the FC is a nano-machine with limited computational capabilities. Furthermore, we analyze the performance of the proposed detectors in terms of their false alarm and missed detection probabilities. Simulation results verify our analytical derivations and reveal interesting insights regarding the trade-off between complexity and performance of the proposed detectors and the considered DTM and STM schemes.

Includes members:
  Member "symbol synchronization for diffusive molecular communication systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "transmitter and receiver architectures for molecular communications a survey on physical design with modulation coding and detection techniques")
  Member "comprehensive reactive receiver modeling for diffusive molecular communication systems reversible binding molecule degradation and finite number of receptors":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "transmitter and receiver architectures for molecular communications a survey on physical design with modulation coding and detection techniques")
  Member "abnormality detection in correlated gaussian molecular nano networks design and analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "abnormality detection inside blood vessels with mobile nanomachines")
  Member "channel estimation for diffusive molecular communications":
    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 "distributed cooperative detection for multi receiver molecular communication":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "symbol by symbol maximum likelihood detection for cooperative molecular communication")
  Member "improving receiver performance of diffusive molecular communication with enzymes":
    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 "modeling and analysis of abnormality detection in biomolecular nano networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "abnormality detection inside blood vessels with mobile nanomachines")
  Member "diffusion based nanonetworking a new modulation technique and performance analysis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "simulation study and analysis of diffusive molecular communications with an apertured plane")

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)