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: "distributed cooperative detection for multi receiver molecular communication"
  Abstract: In this paper, a cooperative diffusion-based molecular communication system is considered where distributed receivers collaboratively determine a transmitter's signal. In this system, the receivers first make local hard decisions about the current transmitted bit and then report these decisions to a fusion center (FC). The FC combines the local hard decisions to make a global decision using an N-out-of-K fusion rule. Asymmetric and symmetric topologies are considered and for each topology, two reporting scenarios, namely, perfect reporting and noisy reporting, are addressed. Closed-form analytical expressions for the expected global error probability are derived for all considered topologies and scenarios. Numerical and simulation results show that system reliability can be greatly improved by combining the detection information of distributed receivers.

Connected through hyperedge HE-1 (induced by source paper "symbol by symbol maximum likelihood detection for cooperative molecular communication"):
  with members:
    - "maximum likelihood detection for cooperative molecular communication"
    - "cooperative abnormality detection via diffusive molecular communications"
    - "convex optimization of distributed cooperative detection in multi receiver molecular communication"
    - "abnormality detection in correlated gaussian molecular nano networks design and analysis"
    - "molecular mimo from theory to prototype"
    - "a comprehensive survey of recent advancements in molecular communication"
    - "analysis and design of multi hop diffusion based molecular communication networks"
    - "joint channel parameter estimation via diffusive molecular communication"

Connected through hyperedge HE-2 (induced by source paper "maximum likelihood detection for cooperative molecular communication"):
  with members:
    - "cooperative abnormality detection via diffusive molecular communications"
    - "convex optimization of distributed cooperative detection in multi receiver molecular communication"
    - "molecular mimo from theory to prototype"
    - "molecular communications channel model and physical layer techniques"
    - "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-3 (induced by source paper "cooperative abnormality detection via diffusive molecular communications"):
  with members:
    - "symbol synchronization for diffusive molecular communication systems"
    - "comprehensive reactive receiver modeling for diffusive molecular communication systems reversible binding molecule degradation and finite number of receptors"
    - "abnormality detection in correlated gaussian molecular nano networks design and analysis"
    - "channel estimation for diffusive molecular communications"
    - "improving receiver performance of diffusive molecular communication with enzymes"
    - "modeling and analysis of abnormality detection in biomolecular nano networks"
    - "diffusion based nanonetworking a new modulation technique and performance analysis"

Connected through hyperedge HE-4 (induced by source paper "convex optimization of distributed cooperative detection in multi receiver molecular communication"):
  with members:
    - "molecular mimo from theory to prototype"
    - "a comprehensive survey of recent advancements in molecular communication"
    - "analysis and design of multi hop diffusion based molecular communication networks"
    - "optimal receiver design for diffusive molecular communication with flow and additive noise"
    - "using dimensional analysis to assess scalability and accuracy in molecular communication"
    - "design and analysis of wireless communication systems using diffusion based molecular communication among bacteria"
    - "extended master equation models for molecular communication networks"

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)