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: "data gathering in networks of bacteria colonies collective sensing and relaying using molecular communication"
  Abstract: The prospect of new biological and industrial applications that require communication in micro-scale, encourages research on the design of bio-compatible communication networks using networking primitives already available in nature. One of the most promising candidates for constructing such networks is to adapt and engineer specific types of bacteria that are capable of sensing, actuation, and above all, communication with each other. In this paper, we describe a new architecture for networks of bacteria to form a data collecting network, as in traditional sensor networks. The key to this architecture is the fact that the node in the network itself is a bacterial colony; as an individual bacterium (biological agent) is a tiny unreliable element with limited capabilities. We describe such a network under two different scenarios. We study the data gathering (sensing and multihop communication) scenario as in sensor networks followed by the consensus problem in a multi-node network. We will explain as to how the bacteria in the colony collectively orchestrate their actions as a node to perform sensing and relaying tasks that would not be possible (at least reliably) by an individual bacterium. Each single bacterium in the colony forms a belief by sensing external parameter (e.g., a molecular signal from another node) from the medium and shares its belief with other bacteria in the colony. Then, after some interactions, all the bacteria in the colony form a common belief and act as a single node. We will model the reception process of each individual bacteria and will study its impact on the overall functionality of a node. We will present results on the reliability of the multihop communication for data gathering scenario as well as the speed of convergence in the consensus scenario.

Connected through hyperedge HE-1 (induced by source paper "design and performance analysis of dual and multi hop diffusive molecular communication systems"):
  with members:
    - "maximum likelihood detection for cooperative molecular communication"
    - "scw codes for maximum likelihood detection in diffusive molecular communications without channel state information"
    - "non coherent detection for diffusive molecular communications"
    - "channel estimation for diffusive molecular communications"
    - "analysis and design of multi hop diffusion based molecular communication networks"
    - "arrival modeling and error analysis for molecular communication via diffusion with drift"
    - "improving receiver performance of diffusive molecular communication with enzymes"
    - "symbol interval optimization for molecular communication with drift"

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 "analysis and design of multi hop diffusion based molecular communication networks"):
  with members:
    - "improving receiver performance of diffusive molecular communication with enzymes"
    - "relaying in diffusion based molecular communication"
    - "a unifying model for external noise sources and isi in diffusive molecular communication"
    - "optimal receiver design for diffusive molecular communication with flow and additive noise"
    - "using dimensional analysis to assess scalability and accuracy in molecular communication"
    - "tabletop molecular communication text messages through chemical signals"

Connected through hyperedge HE-4 (induced by source paper "amplify and forward relaying in two hop diffusion based molecular communication networks"):
  with members:
    - "analysis and design of multi hop diffusion based molecular communication networks"
    - "improving receiver performance of diffusive molecular communication with enzymes"
    - "relaying in diffusion based molecular communication"
    - "a unifying model for external noise sources and isi in diffusive molecular communication"
    - "optimal receiver design for diffusive molecular communication with flow and additive noise"
    - "using dimensional analysis to assess scalability and accuracy in molecular communication"

Connected through hyperedge HE-5 (induced by source paper "a stochastic model for electron transfer in bacterial cables"):
  with members:
    - "diffusion based nanonetworking a new modulation technique and performance analysis"
    - "wireless information transfer with opportunistic energy harvesting"
    - "capacity of discrete molecular diffusion channels"
    - "nanoscale communication with brownian motion"

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

Connected through hyperedge HE-7 (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"
    - "collective sensing capacity of bacteria populations"
    - "molecular communication in fluid media the additive inverse gaussian noise channel"
    - "capacity of discrete molecular diffusion channels"

Connected through hyperedge HE-8 (induced by source paper "molecular communication between two populations of bacteria"):
  with members:
    - "capacity of diffusion based molecular communication with ligand receptors"
    - "collective sensing capacity of bacteria populations"
    - "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)