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: "cooperative abnormality detection via diffusive molecular communications"
  Abstract: In this paper, we consider abnormality detection via diffusive molecular communications (MCs) for a network consisting of several sensors and a fusion center (FC). If a sensor detects an abnormality, it injects into the medium a number of molecules which is proportional to the sensed value. Two transmission schemes for releasing molecules into the medium are considered. In the first scheme, referred to as DTM, each sensor releases a different type of molecule, whereas in the second scheme, referred to as STM, all sensors release the same type of molecule. The molecules released by the sensors propagate through the MC channel and some may reach the FC where the final decision regarding whether or not an abnormality has occurred is made. We derive the optimal decision rules for both DTM and STM. However, the optimal detectors entail high computational complexity as log-likelihood ratios (LLRs) have to be computed. To overcome this issue, we show that the optimal decision rule for STM can be transformed into an equivalent low-complexity decision rule. Since a similar transformation is not possible for DTM, we propose simple low-complexity sub-optimal detectors based on different approximations of the LLR. The proposed low-complexity detectors are more suitable for practical MC systems than the original complex optimal decision rule, particularly when the FC is a nano-machine with limited computational capabilities. Furthermore, we analyze the performance of the proposed detectors in terms of their false alarm and missed detection probabilities. Simulation results verify our analytical derivations and reveal interesting insights regarding the trade-off between complexity and performance of the proposed detectors and the considered DTM and STM schemes.

Connected through hyperedge HE-1 (induced by source paper "abnormality detection inside blood vessels with mobile nanomachines"):
  with members:
    - "on flow induced diffusive mobile molecular communication first hitting time and performance analysis"
    - "stochastic channel modeling for diffusive mobile molecular communication systems"
    - "advanced target detection via molecular communication"
    - "diffusive mobile molecular communications over time variant channels"
    - "modeling duct flow for molecular communication"
    - "abnormality detection in correlated gaussian molecular nano networks design and analysis"
    - "channel estimation for diffusive molecular communications"
    - "joint channel parameter estimation via diffusive molecular communication"

Connected through hyperedge HE-2 (induced by source paper "symbol by symbol maximum likelihood detection for cooperative molecular communication"):
  with members:
    - "maximum likelihood detection for cooperative molecular communication"
    - "convex optimization of distributed cooperative detection in multi receiver molecular communication"
    - "abnormality detection in correlated gaussian molecular nano networks design and analysis"
    - "distributed cooperative detection for multi receiver molecular communication"
    - "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-3 (induced by source paper "on flow induced diffusive mobile molecular communication first hitting time and performance analysis"):
  with members:
    - "a novel experimental platform for in vessel multi chemical molecular communications"
    - "diffusive mobile molecular communications over time variant channels"
    - "a comprehensive survey of recent advancements in molecular communication"
    - "optimal receiver design for diffusive molecular communication with flow and additive noise"
    - "symbol interval optimization for molecular communication with drift"
    - "molecular communication in fluid media the additive inverse gaussian noise channel"

Connected through hyperedge HE-4 (induced by source paper "maximum likelihood detection for cooperative molecular communication"):
  with members:
    - "convex optimization of distributed cooperative detection in multi receiver molecular communication"
    - "distributed cooperative detection for 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-5 (induced by source paper "advanced target detection via molecular communication"):
  with members:
    - "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"
    - "modeling and analysis of abnormality detection in biomolecular nano 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)