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: "error scaling laws for linear optimal estimation from relative measurements"
  Abstract: In this paper, we study the problem of estimating vector-valued variables from noisy ldquorelativerdquo measurements, which arises in sensor network applications. The problem can be posed in terms of a graph, whose nodes correspond to variables and edges to noisy measurements of the difference between two variables. The optimal (minimum variance) linear unbiased estimate of the node variables, with an arbitrary variable as the reference, is considered. This paper investigates how the variance of the estimation error of a node variable grows with the distance of the node to the reference node. A classification of graphs, namely, dense or sparse in Rd, 1 les d les 3 , is established that determines this growth rate. In particular, if a graph is dense in 1-D, 2-D, or 3-D, a node variable's estimation error is upper bounded by a linear, logarithmic, or bounded function of distance from the reference. Corresponding lower bounds are obtained if the graph is sparse in 1-D, 2-D, and 3-D. These results show that naive measures of graph density, such as node degree, are inadequate predictors of the estimation error. Being true for the optimal linear unbiased estimate, these scaling laws determine algorithm-independent limits on the estimation accuracy achievable in large graphs.

Connected through hyperedge HE-1 (induced by source paper "distributed estimation from relative measurements of heterogeneous and uncertain quality"):
  with members:
    - "ergodic randomized algorithms and dynamics over networks"
    - "limited benefit of cooperation in distributed relative localization"
    - "a distributed classification estimation algorithm for sensor networks"

Connected through hyperedge HE-2 (induced by source paper "ergodic randomized algorithms and dynamics over networks"):
  with members:
    - "pagerank optimization by edge selection"
    - "almost sure convergence of a randomized algorithm for relative localization in sensor networks"
    - "convergence of distributed randomized pagerank algorithms"
    - "gossips and prejudices ergodic randomized dynamics in social networks"
    - "limited benefit of cooperation in distributed relative localization"
    - "opinion fluctuations and disagreement in social networks"
    - "distributed randomized algorithms for the pagerank computation"
    - "a web aggregation approach for distributed randomized pagerank algorithms"

Connected through hyperedge HE-3 (induced by source paper "almost sure convergence of a randomized algorithm for relative localization in sensor networks"):
  with members:
    - "opinion fluctuations and disagreement in social networks"
    - "distributed randomized algorithms for the pagerank computation"
    - "a web aggregation approach for distributed randomized pagerank algorithms"

Connected through hyperedge HE-4 (induced by source paper "limited benefit of cooperation in distributed relative localization"):
  with members:
    - "almost sure convergence of a randomized algorithm for relative localization in sensor 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)