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: "policy recognition in the abstract hidden markov model"
  Abstract: In this paper, we present a method for recognising an agent's behaviour in dynamic, noisy, uncertain domains, and across multiple levels of abstraction. We term this problem on-line plan recognition under uncertainty and view it generally as probabilistic inference on the stochastic process representing the execution of the agent's plan. Our contributions in this paper are twofold. In terms of probabilistic inference, we introduce the Abstract Hidden Markov Model (AHMM), a novel type of stochastic processes, provide its dynamic Bayesian network (DBN) structure and analyse the properties of this network. We then describe an application of the Rao-Blackwellised Particle Filter to the AHMM which allows us to construct an efficient, hybrid inference method for this model. In terms of plan recognition, we propose a novel plan recognition framework based on the AHMM as the plan execution model. The Rao-Blackwellised hybrid inference for AHMM can take advantage of the independence properties inherent in a model of plan execution, leading to an algorithm for online probabilistic plan recognition that scales well with the number of levels in the plan hierarchy. This illustrates that while stochastic models for plan execution can be complex, they exhibit special structures which, if exploited, can lead to efficient plan recognition algorithms. We demonstrate the usefulness of the AHMM framework via a behaviour recognition system in a complex spatial environment using distributed video surveillance data.

Connected through hyperedge HE-1 (induced by source paper "plots procedure learning from observations using subtask structure"):
  with members:
    - "composable planning with attributes"
    - "reinforcement learning on web interfaces using workflow guided exploration"
    - "one shot learning of multi step tasks from observation via activity localization in auxiliary video"
    - "taco learning task decomposition via temporal alignment for control"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "behavioral cloning from observation"
    - "one shot visual imitation learning via meta learning"
    - "imitation from observation learning to imitate behaviors from raw video via context translation"

Connected through hyperedge HE-2 (induced by source paper "a transfer learning method for goal recognition exploiting cross domain spatial features"):
  with members:
    - "convolutional neural networks with layer reuse"
    - "deep learning for sensor based activity recognition a survey"
    - "human action recognition and prediction a survey"
    - "transfer learning from speaker verification to multispeaker text to speech synthesis"
    - "a survey on deep transfer learning"
    - "lstm based goal recognition in latent space"
    - "heuristic online goal recognition in continuous domains"
    - "latent intention dialogue models"

Connected through hyperedge HE-3 (induced by source paper "cost based goal recognition meets deep learning"):
  with members:
    - "lstm based goal recognition in latent space"
    - "heuristic online goal recognition in continuous domains"
    - "visual interaction networks"
    - "latent intention dialogue models"
    - "classical planning in deep latent space bridging the subsymbolic symbolic boundary"
    - "value iteration networks"
    - "adam a method for stochastic optimization"
    - "two stream convolutional networks for action recognition in videos"

Connected through hyperedge HE-4 (induced by source paper "autonomous agents modelling other agents a comprehensive survey and open problems"):
  with members:
    - "reasoning about hypothetical agent behaviours and their parameters"
    - "are you doing what i think you are doing criticising uncertain agent models"
    - "e hba using action policies for expert advice and agent typification"
    - "on convergence and optimality of best response learning with policy types in multiagent systems"
    - "an empirical study on the practical impact of prior beliefs over policy types"
    - "heuristic online goal recognition in continuous domains"
    - "a survey of learning in multiagent environments dealing with non stationarity"
    - "belief and truth in hypothesised behaviours"

Connected through hyperedge HE-5 (induced by source paper "lifted filtering via exchangeable decomposition"):
  with members:
    - "sequential lifted bayesian filtering in multiset rewriting systems"
    - "exchangeable variable models"
    - "tractable inference for complex stochastic processes"
    - "rao blackwellised particle filtering for dynamic bayesian networks"
    - "sum product networks a new deep architecture"
    - "sampling first order logical particles"
    - "counting belief propagation"
    - "probabilistic theorem proving"

Connected through hyperedge HE-6 (induced by source paper "multi level discovery of deep options"):
  with members:
    - "learning to repeat fine grained action repetition for deep reinforcement learning"
    - "composing meta policies for autonomous driving using hierarchical deep reinforcement learning"
    - "stochastic neural networks for hierarchical reinforcement learning"
    - "option discovery in hierarchical reinforcement learning using spatio temporal clustering"
    - "learning from the memory of atari 2600"
    - "learning and transfer of modulated locomotor controllers"
    - "the option critic architecture"
    - "hierarchical linearly solvable markov decision problems"

Connected through hyperedge HE-7 (induced by source paper "options discovery with budgeted reinforcement learning"):
  with members:
    - "stochastic neural networks for hierarchical reinforcement learning"
    - "learning and transfer of modulated locomotor controllers"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "variational intrinsic control"
    - "strategic attentive writer for learning macro actions"
    - "hierarchical multiscale recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-8 (induced by source paper "symmetry aware marginal density estimation"):
  with members:
    - "rao blackwellised particle filtering for dynamic bayesian networks"
    - "automorphism groups of graphical models and lifted variational inference"
    - "sum product networks a new deep architecture"
    - "lifted relational variational inference"
    - "markov chains on orbits of permutation groups"
    - "counting belief propagation"
    - "cutset sampling for bayesian 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.AI(Artificial Intelligence)