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: "single bit and reduced dimension diffusion strategies over distributed networks"
  Abstract: We introduce novel diffusion based adaptive estimation strategies for distributed networks that have significantly less communication load and achieve comparable performance to the full information exchange configurations. After local estimates of the desired data is produced in each node, a single bit of information (or a reduced dimensional data vector) is generated using certain random projections of the local estimates. This newly generated data is diffused and then used in neighboring nodes to recover the original full information. We provide the complete state-space description and the mean stability analysis of our algorithms.

Connected through hyperedge HE-1 (induced by source paper "distributed low rank adaptive algorithms based on alternating optimization and applications"):
  with members:
    - "widely linear precoding for large scale mimo with iqi algorithms and performance analysis"
    - "robust adaptive beamforming based on low rank and cross correlation techniques"
    - "dictionary learning over distributed models"
    - "adaptive reduced rank receive processing based on minimum symbol error rate criterion for large scale multiple antenna systems"
    - "robust adaptive beamforming using a low complexity shrinkage based mismatch estimation algorithm"
    - "direction finding algorithms based on joint iterative subspace optimization"
    - "adaptive distributed space time coding based on adjustable code matrices for cooperative mimo relaying systems"
    - "mimo radar using compressive sampling"

Connected through hyperedge HE-2 (induced by source paper "partial diffusion least mean square estimation over networks under noisy information exchange"):
  with members:
    - "compressive diffusion strategies over distributed networks for reduced communication load"
    - "diffusion adaptation over networks"

Connected through hyperedge HE-3 (induced by source paper "compressive diffusion strategies over distributed networks for reduced communication load"):
  with members:
    - "adaptive mixture methods based on bregman divergences"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"
    - "performance limits for distributed estimation over lms adaptive networks"

Connected through hyperedge HE-4 (induced by source paper "distributed low rank estimation based on joint iterative optimization in wireless sensor networks"):
  with members:
    - "sparsity aware adaptive algorithms based on alternating optimization with shrinkage"
    - "blind adaptive interference suppression based on set membership constrained constant modulus algorithms with dynamic bounds"
    - "mimo radar using compressive sampling"

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.SY(Systems and Control)