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: "robust probabilistic inference in distributed systems"
  Abstract: Probabilistic inference problems arise naturally in distributed systems such as sensor networks and teams of mobile robots. Inference algorithms that use message passing are a natural fit for distributed systems, but they must be robust to the failure situations that arise in real-world settings, such as unreliable communication and node failures. Unfortunately, the popular sum-product algorithm can yield very poor estimates in these settings because the nodes' beliefs before convergence can be arbitrarily different from the correct posteriors. In this paper, we present a new message passing algorithm for probabilistic inference which provides several crucial guarantees that the standard sum-product algorithm does not. Not only does it converge to the correct posteriors, but it is also guaranteed to yield a principled approximation at any point before convergence. In addition, the computational complexity of the message passing updates depends only upon the model, and is dependent of the network topology of the distributed system. We demonstrate the approach with detailed experimental results on a distributed sensor calibration task using data from an actual sensor network deployment.

Connected through hyperedge HE-1 (induced by source paper "on local distributed sampling and counting"):
  with members:
    - "distributed symmetry breaking in sampling optimal distributed randomly coloring with fewer colors"
    - "what can be sampled locally"
    - "on derandomizing local distributed algorithms"
    - "uniform sampling through the lov asz local lemma"
    - "on the complexity of local distributed graph problems"
    - "counting hypergraph matchings up to uniqueness threshold"
    - "correlation decay up to uniqueness in spin systems"
    - "computational transition at the uniqueness threshold"

Connected through hyperedge HE-2 (induced by source paper "approximate decentralized bayesian inference"):
  with members:
    - "optimistic concurrency control for distributed unsupervised learning"
    - "decentralized sensor fusion with distributed particle filters"
    - "statistical exponential families a digest with flash cards"

Connected through hyperedge HE-3 (induced by source paper "distributed optimization in wireless sensor networks an island model framework"):
  with members:
    - "ockham s razor in memetic computing three stage optimal memetic exploration"
    - "regression in sensor networks training distributively with alternating projections"
    - "distributed learning in wireless sensor networks"

Connected through hyperedge HE-4 (induced by source paper "decentralized data fusion and active sensing with mobile sensors for modeling and predicting spatiotemporal traffic phenomena"):
  with members:
    - "information theoretic approach to efficient adaptive path planning for mobile robotic environmental sensing"
    - "decentralized sensor fusion with distributed particle filters"
    - "active markov information theoretic path planning for robotic environmental sensing"

Connected through hyperedge HE-5 (induced by source paper "distributed autonomous online learning regrets and intrinsic privacy preserving properties"):
  with members:
    - "relative loss bounds for on line density estimation with the exponential family of distributions"

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.AI(Artificial Intelligence)