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 "high dimensional metric combining for non coherent molecular signal detection"):
  Abstract: In emerging Internet-of-Nano-Thing (IoNT), information will be embedded and conveyed in the form of molecules through complex and diffusive medias. One main challenge lies in the long-tail nature of the channel response causing inter-symbol-interference (ISI), which deteriorates the detection performance. If the channel is unknown, we cannot easily achieve traditional coherent channel estimation and cancellation, and the impact of ISI will be more severe. In this paper, we develop a novel high-dimensional non-coherent scheme for blind detection of molecular signals. We achieve this in a higher-dimensional metric space by combining different non-coherent metrics that exploit the transient features of the signals. By deducing the theoretical bit error rate (BER) for any constructed high-dimensional non-coherent metric, we prove that, higher dimensionality always achieves a lower BER in the same sample space. Then, we design a generalised blind detection algorithm that utilizes the Parzen approximation and its probabilistic neural network (Parzen-PNN) to detect information bits. Taking advantages of its fast convergence and parallel implementation, our proposed scheme can meet the needs of detection accuracy and real-time computing. Numerical simulations demonstrate that our proposed scheme can gain 10dB BER compared with other state of the art methods.

Includes members:
  Member "effective enzyme deployment for degradation of interference molecules in molecular communication":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "two way molecular communications")
  Member "active versus passive receiver model transforms for diffusive molecular communication":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "end to end performance optimization in hybrid molecular and electromagnetic communications")
  Member "chemical propagation pattern for molecular communications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "spherical diffusion model with semi permeable boundary a transfer function approach")
  Member "low complexity noncoherent signal detection for nanoscale 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 "analysis and design of multi hop diffusion based molecular communication networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "end to end performance optimization in hybrid molecular and electromagnetic communications")
  Member "molecular communications channel model and physical layer techniques":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey of biological building blocks for synthetic molecular communication systems")
  Member "improving receiver performance of diffusive molecular communication with enzymes":
    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 "optimal receiver design for diffusive molecular communication with flow and additive noise":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "transmitter and receiver architectures for molecular communications a survey on physical design with modulation coding and detection techniques")

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.LG(Machine Learning)