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: "diffusive molecular communications with reactive molecules channel modeling and signal design"
  Abstract: This paper focuses on molecular communication (MC) systems using two types of signaling molecules which may participate in a reversible bimolecular reaction in the channel. The motivation for studying these MC systems is that they can realize the concept of constructive and destructive signal superposition, which leads to favorable properties such as inter-symbol interference (ISI) reduction and avoiding environmental contamination due to continuous release of signaling molecules into the channel. This work first presents a general formulation for binary modulation schemes that employ two types of signaling molecules and proposes several modulation schemes as special cases. Moreover, two types of receivers are considered: a receiver that is able to observe both types of signaling molecules (2TM), and a simpler receiver that can observe only one type of signaling molecules (1TM). For both of these receivers, the maximum likelihood (ML) detector for general binary modulation is derived under the assumption that the detector has perfect knowledge of the ISI-causing sequence. In addition, two suboptimal detectors of different complexity are proposed, namely an ML-based detector that employs an estimate of the ISI-causing sequence and a detector that neglects the effect of ISI. The proposed detectors, except for the detector that neglects ISI for the 2TM receiver, require knowledge of the channel response (CR) of the considered MC system. Moreover, the CR is needed for performance evaluation of all proposed detectors. However, deriving the CR of MC systems with reactive signaling molecules is challenging since the underlying partial differential equations that describe the reaction-diffusion mechanism are coupled and non-linear. Therefore, we develop an algorithm for efficient computation of the CR and validate its accuracy via particle-based simulation.

Connected through hyperedge HE-1 (induced by source paper "a survey of biological building blocks for synthetic molecular communication systems"):
  with members:
    - "transmitter and receiver architectures for molecular communications a survey on physical design with modulation coding and detection techniques"
    - "designing molecular circuits for approximate maximum a posteriori demodulation of concentration modulated signals"
    - "index modulation for molecular communication via diffusion systems"
    - "channel modeling for diffusive molecular communication a tutorial review"
    - "a novel experimental platform for in vessel multi chemical molecular communications"
    - "diffusion based molecular communication principle key technologies and challenges"
    - "symbol synchronization for diffusion based molecular communications"
    - "applications of molecular communications to medicine a survey"

Connected through hyperedge HE-2 (induced by source paper "chemical reactions based detection mechanism for molecular communications"):
  with members:
    - "chemical reactions based microfluidic transmitter and receiver for molecular communication"
    - "channel modeling for diffusive molecular communication a tutorial review"
    - "on the design of matched filters for molecule counting receivers"
    - "effective enzyme deployment for degradation of interference molecules in molecular communication"
    - "on medium chemical reaction in diffusion based molecular communication a two way relaying example"
    - "improving receiver performance of diffusive molecular communication with enzymes"
    - "extended master equation models for molecular communication networks"
    - "tabletop molecular communication text messages through chemical signals"

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)