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: "measurement partitioning and observational equivalence in state estimation"
  Abstract: This letter studies measurement partitioning and equivalence in state estimation based on graph-theoretic principles. We show that a set of critical measurements (required to ensure LTI state-space observability) can be further partitioned into two types:~$\alpha$ and~$\beta$. This partitioning is driven by different graphical (or algebraic) methods used to define the corresponding measurements. Subsequently, we describe observational equivalence, i.e. given an~$\alpha$ (or~$\beta$) measurement, say~$y_i$, what is the set of measurements equivalent to~$y_i$, such that only one measurement in this set is required to ensure observability? Since~$\alpha$ and~$\beta$ measurements are cast using different algebraic and graphical characteristics, their equivalence sets are also derived using different algebraic and graph-theoretic principles. We illustrate the related concepts on an appropriate system digraph.

Connected through hyperedge HE-1 (induced by source paper "on the controllability of clustered scale free networks"):
  with members:
    - "cyber social systems modeling inference and optimal design"
    - "distributed estimation recovery under sensor failure"
    - "controllability of social networks and the strategic use of random information"
    - "observational equivalence in system estimation contractions in complex networks"
    - "control principles of complex networks"
    - "graph theoretic distributed inference in social networks"
    - "on the genericity properties in distributed estimation topology design and sensor placement"
    - "multi agent consensus with relative state dependent measurement noises"

Connected through hyperedge HE-2 (induced by source paper "cyber social systems modeling inference and optimal design"):
  with members:
    - "structural cost optimal design of sensor networks for distributed estimation"
    - "distributed estimation recovery under sensor failure"
    - "observational equivalence in system estimation contractions in complex networks"
    - "active sensing of social networks"
    - "graph theoretic distributed inference in social networks"
    - "on the genericity properties in distributed estimation topology design and sensor placement"
    - "gossip algorithms for distributed signal processing"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"

Connected through hyperedge HE-3 (induced by source paper "structural cost optimal design of sensor networks for distributed estimation"):
  with members:
    - "distributed estimation recovery under sensor failure"
    - "graph theoretic distributed inference in social networks"
    - "on the genericity properties in distributed estimation topology design and sensor placement"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive 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)