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: "tractable inference for complex stochastic processes"
  Abstract: The monitoring and control of any dynamic system depends crucially on the ability to reason about its current status and its future trajectory. In the case of a stochastic system, these tasks typically involve the use of a belief state- a probability distribution over the state of the process at a given point in time. Unfortunately, the state spaces of complex processes are very large, making an explicit representation of a belief state intractable. Even in dynamic Bayesian networks (DBNs), where the process itself can be represented compactly, the representation of the belief state is intractable. We investigate the idea of maintaining a compact approximation to the true belief state, and analyze the conditions under which the errors due to the approximations taken over the lifetime of the process do not accumulate to make our answers completely irrelevant. We show that the error in a belief state contracts exponentially as the process evolves. Thus, even with multiple approximations, the error in our process remains bounded indefinitely. We show how the additional structure of a DBN can be used to design our approximation scheme, improving its performance significantly. We demonstrate the applicability of our ideas in the context of a monitoring task, showing that orders of magnitude faster inference can be achieved with only a small degradation in accuracy.

Connected through hyperedge HE-1 (induced by source paper "explaining deep neural networks with a polynomial time algorithm for shapley values approximation"):
  with members:
    - "lightweight probabilistic deep networks"
    - "explanation methods in deep learning users values concerns and challenges"
    - "a theoretical explanation for perplexing behaviors of backpropagation based visualizations"
    - "sanity checks for saliency maps"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "learning important features through propagating activation differences"
    - "towards better understanding of gradient based attribution methods for deep neural networks"
    - "visualizing deep neural network decisions prediction difference analysis"

Connected through hyperedge HE-2 (induced by source paper "a general framework for uncertainty estimation in deep learning"):
  with members:
    - "a 64 mw dnn based visual navigation engine for autonomous nano drones"
    - "where is the information in a deep neural network"
    - "lightweight probabilistic deep networks"
    - "deep drone racing learning agile flight in dynamic environments"
    - "the limits and potentials of deep learning for robotics"
    - "ensemble bayesian decision making with redundant deep perceptual control policies"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "towards safe autonomous driving capture uncertainty in the deep neural network for lidar 3d vehicle detection"

Connected through hyperedge HE-3 (induced by source paper "a rate distortion framework for explaining neural network decisions"):
  with members:
    - "the computational complexity of understanding network decisions"
    - "lightweight probabilistic deep networks"
    - "innvestigate neural networks"
    - "methods for interpreting and understanding deep neural networks"
    - "smoothgrad removing noise by adding noise"
    - "a unified approach to interpreting model predictions"
    - "learning important features through propagating activation differences"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-4 (induced by source paper "reasoning about hypothetical agent behaviours and their parameters"):
  with members:
    - "on convergence and optimality of best response learning with policy types in multiagent systems"
    - "belief and truth in hypothesised behaviours"
    - "exploiting causality for selective belief filtering in dynamic bayesian networks"
    - "a game theoretic model and best response learning method for ad hoc coordination in multiagent systems"
    - "team behavior in interactive dynamic influence diagrams with applications to ad hoc teams"
    - "bayesopt a bayesian optimization library for nonlinear optimization experimental design and bandits"
    - "the factored frontier algorithm for approximate inference in dbns"
    - "bayes bluff opponent modelling in poker"

Connected through hyperedge HE-5 (induced by source paper "advantage amplification in slowly evolving latent state environments"):
  with members:
    - "reinforcement learning based recommender system using biclustering technique"
    - "deep reinforcement learning for list wise recommendations"
    - "learning to repeat fine grained action repetition for deep reinforcement learning"
    - "bridging the gap between value and policy based reinforcement learning"
    - "reinforcement learning with a corrupted reward channel"
    - "taming the noise in reinforcement learning via soft updates"
    - "trust region policy optimization"
    - "hierarchical solution of markov decision processes using macro actions"

Connected through hyperedge HE-6 (induced by source paper "factored probabilistic belief tracking"):
  with members:
    - "stochastic simulation algorithms for dynamic probabilistic networks"
    - "context specific independence in bayesian networks"
    - "a general non probabilistic theory of inductive reasoning"
    - "the factored frontier algorithm for approximate inference in dbns"
    - "rao blackwellised particle filtering for dynamic bayesian networks"
    - "on the relation between kappa calculus and probabilistic reasoning"
    - "a simple insight into iterative belief propagation s success"

Connected through hyperedge HE-7 (induced by source paper "a sufficient statistic for influence in structured multiagent environments"):
  with members:
    - "near optimal behavior via approximate state abstraction"
    - "model reduction techniques for computing approximately optimal solutions for markov decision processes"
    - "a bilinear programming approach for multiagent planning"
    - "optimal and approximate q value functions for decentralized pomdps"
    - "decision theoretic planning structural assumptions and computational leverage"

Connected through hyperedge HE-8 (induced by source paper "efficient decision making and belief space planning using sparse approximations"):
  with members:
    - "complexity analysis and efficient measurement selection primitives for high rate graph slam"
    - "approximate planning for factored pomdps using belief state simplification"
    - "sampling based algorithms for optimal motion planning"
    - "finding approximate pomdp solutions through belief compression"

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)