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: "convex optimization of distributed cooperative detection in multi receiver molecular communication"
  Abstract: In this paper, the error performance achieved by cooperative detection among K distributed receivers in a diffusion-based molecular communication (MC) system is analyzed and optimized. In this system, the receivers first make local hard decisions on the transmitted symbol 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. Two reporting scenarios, namely, perfect reporting and noisy reporting, are considered. Closed-form expressions are derived for the expected global error probability of the system for both reporting scenarios. New approximated expressions are also derived for the expected error probability. Convex constraints are then found to make the approximated expressions jointly convex with respect to the decision thresholds at the receivers and the FC. Based on such constraints, suboptimal convex optimization problems are formulated and solved to determine the optimal decision thresholds which minimize the expected error probability of the system. Numerical and simulation results reveal that the system error performance is greatly improved by combining the detection information of distributed receivers. They also reveal that the solutions to the formulated suboptimal convex optimization problems achieve near-optimal global error performance.

Connected through hyperedge HE-1 (induced by source paper "using spatial partitioning to reduce the bit error rate of diffusion based molecular communications"):
  with members:
    - "designing molecular circuits for approximate maximum a posteriori demodulation of concentration modulated signals"
    - "generalized solution for the demodulation of reaction shift keying signals in molecular communication networks"
    - "on the design of matched filters for molecule counting receivers"
    - "improving the capacity of molecular communication using enzymatic reaction cycles"
    - "on medium chemical reaction in diffusion based molecular communication a two way relaying example"
    - "a comprehensive survey of recent advancements in molecular communication"
    - "a markovian approach to the optimal demodulation of diffusion based molecular communication networks"
    - "improving receiver performance of diffusive molecular communication with enzymes"

Connected through hyperedge HE-2 (induced by source paper "channel characterization for 1d molecular communication with two absorbing receivers"):
  with members:
    - "a comprehensive survey of recent advancements in molecular communication"
    - "effect of degradation in molecular communication impairment or enhancement"

Connected through hyperedge HE-3 (induced by source paper "channel modeling for diffusive molecular communication a tutorial review"):
  with members:
    - "a survey of biological building blocks for synthetic molecular communication systems"
    - "stochastic channel modeling for diffusive mobile molecular communication systems"
    - "a novel experimental platform for in vessel multi chemical molecular communications"
    - "generalized solution for the demodulation of reaction shift keying signals in molecular communication networks"
    - "diffusive mobile molecular communications over time variant channels"
    - "symbol synchronization for diffusion based molecular communications"
    - "effective enzyme deployment for degradation of interference molecules in molecular communication"
    - "modeling duct flow for molecular communication"

Connected through hyperedge HE-4 (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"
    - "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-5 (induced by source paper "maximum likelihood detection for cooperative molecular communication"):
  with members:
    - "cooperative abnormality detection via diffusive molecular communications"
    - "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-6 (induced by source paper "distributed cooperative detection for multi receiver molecular communication"):
  with members:
    - "maximum likelihood detection for cooperative 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"
    - "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"

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)