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: "collective sensing capacity of bacteria populations"
  Abstract: The design of biological networks using bacteria as the basic elements of the network is initially motivated by a phenomenon called quorum sensing. Through quorum sensing, each bacterium performs sensing the medium and communicating it to others via molecular communication. As a result, bacteria can orchestrate and act collectively and perform tasks impossible otherwise. In this paper, we consider a population of bacteria as a single node in a network. In our version of biological communication networks, such a node would communicate with one another via molecular signals. As a first step toward such networks, this paper focuses on the study of the transfer of information to the population (i.e., the node) by stimulating it with a concentration of special type of a molecules signal. These molecules trigger a chain of processes inside each bacteria that results in a final output in the form of light or fluorescence. Each stage in the process adds noise to the signal carried to the next stage. Our objective is to measure (compute) the maximum amount of information that we can transfer to the node. This can be viewed as the collective sensing capacity of the node. The molecular concentration, which carries the information, is the input to the node, which should be estimated by observing the produced light as the output of the node (i.e., the entire population of bacteria forming the node). We focus on the noise caused by the random process of trapping molecules at the receptors as well as the variation of outputs of different bacteria in the node. The capacity variation with the number of bacteria in the node and the number of receptors per bacteria is obtained. Finally, we investigated the collective sensing capability of the node when a specific form of molecular signaling concentration is used.

Connected through hyperedge HE-1 (induced by source paper "information theory of molecular communication directions and challenges"):
  with members:
    - "shannon capacity of signal transduction for multiple independent receptors"
    - "capacity limits of diffusion based molecular timing channels"
    - "comprehensive reactive receiver modeling for diffusive molecular communication systems reversible binding molecule degradation and finite number of receptors"
    - "type based sign modulation for molecular communication"
    - "a comprehensive survey of recent advancements in molecular communication"
    - "information friction and its implications on minimum energy required for communication"
    - "a perspective on future research directions in information theory"
    - "upper bound on the capacity of a cascade of nonlinear and noisy channels"

Connected through hyperedge HE-2 (induced by source paper "a comprehensive survey of recent advancements in molecular communication"):
  with members:
    - "molecular mimo from theory to prototype"
    - "molecular communications channel model and physical layer techniques"
    - "a universal channel model for molecular communication systems with metal oxide detectors"
    - "design and optimizing of on chip kinesin substrates for molecular communication"
    - "improving receiver performance of diffusive molecular communication with enzymes"
    - "a molecular communication link for monitoring in confined environments"
    - "relaying in diffusion based molecular communication"
    - "effect of receptor density and size on signal reception in molecular communication via diffusion with an absorbing receiver"

Connected through hyperedge HE-3 (induced by source paper "relaying in diffusion based molecular communication"):
  with members:
    - "capacity of diffusion based molecular communication with ligand receptors"
    - "data gathering in networks of bacteria colonies collective sensing and relaying using molecular communication"
    - "molecular communication in fluid media the additive inverse gaussian noise channel"
    - "capacity of discrete molecular diffusion channels"

Connected through hyperedge HE-4 (induced by source paper "the unreasonable effectiveness of blood pressure measurement molecular communication in biological systems"):
  with members:
    - "variance constrained capacity of the molecular timing channel with synchronization error"
    - "improving receiver performance of diffusive molecular communication with enzymes"

Connected through hyperedge HE-5 (induced by source paper "design and analysis of wireless communication systems using diffusion based molecular communication among bacteria"):
  with members:
    - "relaying in diffusion based molecular communication"
    - "molecular communication between two populations of bacteria"
    - "capacity of diffusion based molecular communication with ligand receptors"
    - "data gathering in networks of bacteria colonies collective sensing and relaying using molecular communication"
    - "molecular communication in fluid media the additive inverse gaussian noise channel"
    - "capacity of discrete molecular diffusion channels"

Connected through hyperedge HE-6 (induced by source paper "molecular communication between two populations of bacteria"):
  with members:
    - "capacity of diffusion based molecular communication with ligand receptors"
    - "data gathering in networks of bacteria colonies collective sensing and relaying using molecular communication"
    - "molecular communication in fluid media the additive inverse gaussian noise channel"
    - "capacity of discrete molecular diffusion channels"

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)