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: "distributing the kalman filter for large scale systems"
  Abstract: This paper presents a distributed Kalman filter to estimate the state of a sparsely connected, large-scale, n -dimensional, dynamical system monitored by a network of N sensors. Local Kalman filters are implemented on nl-dimensional subsystems, nl Lt n, obtained by spatially decomposing the large-scale system. The distributed Kalman filter is optimal under an Lth order Gauss-Markov approximation to the centralized filter. We quantify the information loss due to this Lth-order approximation by the divergence, which decreases as L increases. The order of the approximation L leads to a bound on the dimension of the subsystems, hence, providing a criterion for subsystem selection. The (approximated) centralized Riccati and Lyapunov equations are computed iteratively with only local communication and low-order computation by a distributed iterate collapse inversion (DICI) algorithm. We fuse the observations that are common among the local Kalman filters using bipartite fusion graphs and consensus averaging algorithms. The proposed algorithm achieves full distribution of the Kalman filter. Nowhere in the network, storage, communication, or computation of n-dimensional vectors and matrices is required; only nl Lt n dimensional vectors and matrices are communicated or used in the local computations at the sensors. In other words, knowledge of the state is itself distributed.

Connected through hyperedge HE-1 (induced by source paper "coded distributed tracking"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "coded iterative computing using substitute decoding"
    - "implicit cooperative positioning in vehicular networks"
    - "block diagonal and lt codes for distributed computing with straggling servers"
    - "minimizing the age of the information through queues"
    - "a unified coding framework for distributed computing with straggling servers"

Connected through hyperedge HE-2 (induced by source paper "coordinate descent diffusion learning by networked agents"):
  with members:
    - "asynchronous adaptation and learning over networks part iii comparison analysis"
    - "asynchronous adaptation and learning over networks part ii performance analysis"
    - "asynchronous adaptation and learning over networks part i modeling and stability analysis"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"
    - "a sparsity promoting adaptive algorithm for distributed learning"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "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 "byzantine resilient distributed observers for lti systems"):
  with members:
    - "a distributed observer for a time invariant linear system"
    - "secure distributed state estimation of an lti system over time varying networks and analog erasure channels"
    - "detection and mitigation of biasing attacks on distributed estimation networks"
    - "detection of biasing attacks on distributed estimation networks"
    - "distributed observers for lti systems"
    - "iterative approximate byzantine consensus in arbitrary directed graphs"

Connected through hyperedge HE-4 (induced by source paper "accuracy analysis for distributed weighted least squares estimation in finite steps and loopy networks"):
  with members:
    - "on the controllability and observability of networked dynamic systems"

Connected through hyperedge HE-5 (induced by source paper "consensus innovations distributed kalman filter with optimized gains"):
  with members:
    - "distributed kalman filtering over massive data sets analysis through large deviations of random riccati equations"
    - "on the genericity properties in distributed estimation topology design and sensor placement"
    - "gossip and distributed kalman filtering weak consensus under weak detectability"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "networked estimation under information constraints"
    - "gossip algorithms for distributed signal processing"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"

Connected through hyperedge HE-6 (induced by source paper "d slats distributed simultaneous localization and time synchronization"):
  with members:
    - "realizing uncertainty aware timing stack in embedded operating system"
    - "joint ranging and clock parameter estimation by wireless round trip time measurements"
    - "joint localization and clock synchronization for wireless sensor networks"

Connected through hyperedge HE-7 (induced by source paper "consistent distributed state estimation with global observability over sensor network"):
  with members:
    - "infinite horizon optimal transmission power control for remote state estimation over fading channels"
    - "partition based distributed kalman filter with plug and play features"

Connected through hyperedge HE-8 (induced by source paper "diffusion adaptation over multi agent networks with wireless link impairments"):
  with members:
    - "diffusion lms strategies in sensor networks with noisy input data"
    - "estimation of space time varying parameters using a diffusion lms algorithm"
    - "diffusion adaptation over networks"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "weight optimization for consensus algorithms with correlated switching topology"
    - "distributed consensus algorithms in sensor networks link failures and channel noise"
    - "distributed consensus algorithms in sensor networks quantized data and random link failures"

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)