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: "consensus problem under diffusion based molecular communication"
  Abstract: We investigate the consensus problem in a network where nodes communicate via diffusion-based molecular communication (DbMC). In DbMC, messages are conveyed via the variation in the concentration of molecules in the medium. Every node acquires sensory information about the environment. Communication enables the nodes to reach the best estimate for that measurement, e.g., the average of the initial estimates by all nodes. We consider an iterative method for communication among nodes that enables information spreading and averaging in the network. We show that the consensus can be attained after a finite number of iterations and variance of estimates of nodes can be made arbitrarily small via communication.

Connected through hyperedge HE-1 (induced by source paper "queuing models for abstracting interactions in bacterial communities"):
  with members:
    - "capacity of electron based communication over bacterial cables the full csi case"
    - "a stochastic model for electron transfer in bacterial cables"
    - "relaying in diffusion based molecular communication"
    - "optimal receiver design for diffusive molecular communication with flow and additive noise"
    - "design and analysis of wireless communication systems using diffusion based molecular communication among bacteria"
    - "diffusion based nanonetworking a new modulation technique and performance analysis"
    - "molecular communication in fluid media the additive inverse gaussian noise channel"
    - "capacity of discrete molecular diffusion channels"

Connected through hyperedge HE-2 (induced by source paper "capacity of diffusion based molecular communication with ligand receptors"):
  with members:
    - "capacity of discrete molecular diffusion channels"
    - "molecular communication physically realistic models and achievable information rates"

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

Connected through hyperedge HE-4 (induced by source paper "capacity of discrete molecular diffusion channels"):
  with members:
    - "molecular communication in fluid media the additive inverse gaussian noise channel"
    - "molecular communication physically realistic models and achievable information rates"

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)