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: "learning finite state representations of recurrent policy networks"
  Abstract: Recurrent neural networks (RNNs) are an effective representation of control policies for a wide range of reinforcement and imitation learning problems. RNN policies, however, are particularly difficult to explain, understand, and analyze due to their use of continuous-valued memory vectors and observation features. In this paper, we introduce a new technique, Quantized Bottleneck Insertion, to learn finite representations of these vectors and features. The result is a quantized representation of the RNN that can be analyzed to improve our understanding of memory use and general behavior. We present results of this approach on synthetic environments and six Atari games. The resulting finite representations are surprisingly small in some cases, using as few as 3 discrete memory states and 10 observations for a perfect Pong policy. We also show that these finite policy representations lead to improved interpretability.

Connected through hyperedge HE-1 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence"):
  with members:
    - "memo a deep network for flexible combination of episodic memories"
    - "towards a human like open domain chatbot"
    - "learning higher order logic programs"
    - "solving rubik s cube with a robot hand"
    - "genesis generative scene inference and sampling with object centric latent representations"
    - "on the measure of intelligence"
    - "entity abstraction in visual model based reinforcement learning"
    - "emergent systematic generalization in a situated agent"

Connected through hyperedge HE-2 (induced by source paper "the emerging landscape of explainable ai planning and decision making"):
  with members:
    - "explainable reinforcement learning through a causal lens"
    - "generation of policy level explanations for reinforcement learning"
    - "expectation aware planning a unifying framework for synthesizing and executing self explaining plans for human aware planning"
    - "towards explainable ai planning as a service"
    - "exploring computational user models for agent policy summarization"
    - "explicability legibility predictability transparency privacy security the emerging landscape of interpretable agent behavior"
    - "contrastive explanation a structural model approach"
    - "explanation in artificial intelligence insights from the social sciences"

Connected through hyperedge HE-3 (induced by source paper "neural symbolic descriptive action model from images the search for strips"):
  with members:
    - "unsupervised grounding of plannable first order logic representation from images"
    - "learning action models from disordered and noisy plan traces"
    - "towards stable symbol grounding with zero suppressed state autoencoder"
    - "learning strips action models with classical planning"
    - "learning plannable representations with causal infogan"
    - "extracting action sequences from texts based on deep reinforcement learning"
    - "dvae discrete variational autoencoders with overlapping transformations"
    - "lstm based goal recognition in latent space"

Connected through hyperedge HE-4 (induced by source paper "a2 extracting cyclic switchings from dob nets for rejecting excessive disturbances"):
  with members:
    - "dob net actively rejecting unknown excessive time varying disturbances"
    - "particle filter networks with application to visual localization"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "extracting automata from recurrent neural networks using queries and counterexamples"
    - "deepnav learning to navigate large cities"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "continuous deep q learning with model based acceleration"
    - "q prop sample efficient policy gradient with an off policy critic"

Connected through hyperedge HE-5 (induced by source paper "learning causal state representations of partially observable environments"):
  with members:
    - "efficient model based deep reinforcement learning with variational state tabulation"
    - "learning plannable representations with causal infogan"
    - "deep variational reinforcement learning for pomdps"
    - "an empirical evaluation of rule extraction from recurrent neural networks"
    - "extracting automata from recurrent neural networks using queries and counterexamples"
    - "rule extraction algorithm for deep neural networks a review"
    - "deep recurrent q learning for partially observable mdps"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

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.LG(Machine Learning)