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: "model reduction techniques for computing approximately optimal solutions for markov decision processes"
  Abstract: We present a method for solving implicit (factored) Markov decision processes (MDPs) with very large state spaces. We introduce a property of state space partitions which we call epsilon-homogeneity. Intuitively, an epsilon-homogeneous partition groups together states that behave approximately the same under all or some subset of policies. Borrowing from recent work on model minimization in computer-aided software verification, we present an algorithm that takes a factored representation of an MDP and an 0<=epsilon<=1 and computes a factored epsilon-homogeneous partition of the state space. This partition defines a family of related MDPs - those MDPs with state space equal to the blocks of the partition, and transition probabilities "approximately" like those of any (original MDP) state in the source block. To formally study such families of MDPs, we introduce the new notion of a "bounded parameter MDP" (BMDP), which is a family of (traditional) MDPs defined by specifying upper and lower bounds on the transition probabilities and rewards. We describe algorithms that operate on BMDPs to find policies that are approximately optimal with respect to the original MDP. In combination, our method for reducing a large implicit MDP to a possibly much smaller BMDP using an epsilon-homogeneous partition, and our methods for selecting actions in BMDPs constitute a new approach for analyzing large implicit MDPs. Among its advantages, this new approach provides insight into existing algorithms to solving implicit MDPs, provides useful connections to work in automata theory and model minimization, and suggests methods, which involve varying epsilon, to trade time and space (specifically in terms of the size of the corresponding state space) for solution quality.

Connected through hyperedge HE-1 (induced by source paper "learning discrete state abstractions with deep variational inference"):
  with members:
    - "minatar an atari inspired testbed for more efficient reinforcement learning experiments"
    - "online abstraction with mdp homomorphisms for deep learning"
    - "transfer with model features in reinforcement learning"
    - "near optimal behavior via approximate state abstraction"
    - "distral robust multitask reinforcement learning"
    - "deep variational information bottleneck"
    - "prioritized experience replay"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-2 (induced by source paper "model based reinforcement learning exploiting state action equivalence"):
  with members:
    - "efficient bias span constrained exploration exploitation in reinforcement learning"
    - "near optimal behavior via approximate state abstraction"
    - "unifying pac and regret uniform pac bounds for episodic reinforcement learning"
    - "selecting near optimal approximate state representations in reinforcement learning"
    - "sample complexity of multi task reinforcement learning"
    - "metrics for finite markov decision processes"
    - "regal a regularization based algorithm for reinforcement learning in weakly communicating mdps"

Connected through hyperedge HE-3 (induced by source paper "multi step greedy and approximate real time dynamic programming"):
  with members:
    - "tight regret bounds for model based reinforcement learning with greedy policies"
    - "how to combine tree search methods in reinforcement learning"
    - "near optimal behavior via approximate state abstraction"
    - "variance reduced value iteration and faster algorithms for solving markov decision processes"
    - "unifying pac and regret uniform pac bounds for episodic reinforcement learning"
    - "learning in real time search a unifying framework"

Connected through hyperedge HE-4 (induced by source paper "a sufficient statistic for influence in structured multiagent environments"):
  with members:
    - "near optimal behavior via approximate state abstraction"
    - "tractable inference for complex stochastic 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-5 (induced by source paper "the bottleneck simulator a model based deep reinforcement learning approach"):
  with members:
    - "integrating planning for task completion dialogue policy learning"
    - "goal driven dynamics learning via bayesian optimization"
    - "iterative policy learning in end to end trainable task oriented neural dialog models"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "near optimal behavior via approximate state abstraction"
    - "schema networks zero shot transfer with a generative causal model of intuitive physics"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "deal or no deal end to end learning for negotiation dialogues"

Connected through hyperedge HE-6 (induced by source paper "deictic image maps an abstraction for learning pose invariant manipulation policies"):
  with members:
    - "learning task oriented grasping for tool manipulation from simulated self supervision"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "pick and place without geometric object models"
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"
    - "high precision grasp pose detection in dense clutter"
    - "prioritized experience replay"
    - "continuous control with deep reinforcement learning"
    - "dueling network architectures for deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "computational approaches for stochastic shortest path on succinct mdps"):
  with members:
    - "optimizing quantiles in preference based markov decision processes"
    - "efficient solution algorithms for factored mdps"

Connected through hyperedge HE-8 (induced by source paper "agent agnostic human in the loop reinforcement learning"):
  with members:
    - "near optimal behavior via approximate state abstraction"
    - "combating deep reinforcement learning s sisyphean curse with intrinsic fear"
    - "alternating optimisation and quadrature for robust control"
    - "theoretically grounded policy advice from multiple teachers in reinforcement learning settings with applications to negative transfer"
    - "benchmarking deep reinforcement learning for continuous control"
    - "domain independent optimistic initialization for reinforcement learning"
    - "safe exploration of state and action spaces in reinforcement learning"
    - "safe exploration in markov decision processes"

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)