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: "value function approximations for partially observable markov decision processes"
  Abstract: Partially observable Markov decision processes (POMDPs) provide an elegant mathematical framework for modeling complex decision and planning problems in stochastic domains in which states of the system are observable only indirectly, via a set of imperfect or noisy observations. The modeling advantage of POMDPs, however, comes at a price -- exact methods for solving them are computationally very expensive and thus applicable in practice only to very simple problems. We focus on efficient approximation (heuristic) methods that attempt to alleviate the computational problem and trade off accuracy for speed. We have two objectives here. First, we survey various approximation methods, analyze their properties and relations and provide some new insights into their differences. Second, we present a number of new approximation methods and novel refinements of existing techniques. The theoretical results are supported by experiments on a problem from the agent navigation domain.

Connected through hyperedge HE-1 (induced by source paper "point based methods for model checking in partially observable markov decision processes"):
  with members:
    - "verification of uncertain pomdps using barrier certificates"
    - "bounded policy synthesis for pomdps with safe reachability objectives"
    - "a storm is coming a modern probabilistic model checker"
    - "inverse reward design"
    - "qualitative analysis of pomdps with temporal logic specifications for robotics applications"
    - "a learning based approach to control synthesis of markov decision processes for linear temporal logic specifications"
    - "what is decidable about partially observable markov decision processes with omega regular objectives"
    - "a theory of goal oriented mdps with dead ends"

Connected through hyperedge HE-2 (induced by source paper "shaping belief states with generative environment models for rl"):
  with members:
    - "conditional adversarial generative flow for controllable image synthesis"
    - "preventing posterior collapse with delta vaes"
    - "learning latent dynamics for planning from pixels"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "optimizing agent behavior over long time scales by transporting value"
    - "learning attractor dynamics for generative memory"
    - "geometric consistency for self supervised end to end visual odometry"
    - "learning and querying fast generative models for reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "learning belief representations for imitation learning in pomdps"):
  with members:
    - "neural predictive belief representations"
    - "learning and querying fast generative models for reinforcement learning"
    - "deep variational reinforcement learning for pomdps"
    - "recurrent world models facilitate policy evolution"
    - "representation learning with contrastive predictive coding"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-4 (induced by source paper "optimizing sequential medical treatments with auto encoding heuristic search in pomdps"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "representation balancing mdps for off policy policy evaluation"
    - "distributed prioritized experience replay"
    - "deep variational reinforcement learning for pomdps"
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "distributed distributional deterministic policy gradients"
    - "janossy pooling learning deep permutation invariant functions for variable size inputs"
    - "filtering variational objectives"

Connected through hyperedge HE-5 (induced by source paper "artificial intelligence for low resource communities influence maximization in an uncertain world"):
  with members:
    - "learning in pomdps with monte carlo tree search"
    - "activating the breakfast club modeling influence spread in natural world social networks"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "incremental pruning a simple fast exact method for partially observable markov decision processes"
    - "maximizing social influence in nearly optimal time"
    - "community structure and scale free collections of erdős renyi graphs"
    - "information theoretic measures of influence based on content dynamics"

Connected through hyperedge HE-6 (induced by source paper "non cooperative inverse reinforcement learning"):
  with members:
    - "policy optimization provably converges to nash equilibria in zero sum linear quadratic games"
    - "actor critic policy optimization in partially observable multiagent environments"
    - "finite sample analyses for fully decentralized multi agent reinforcement learning"
    - "an efficient generalized bellman update for cooperative inverse reinforcement learning"
    - "finite sample analysis for decentralized batch multi agent reinforcement learning with networked agents"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "cooperative inverse reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "control theory meets pomdps a hybrid systems approach"):
  with members:
    - "safe policy synthesis in multi agent pomdps via discrete time barrier functions"
    - "verification of uncertain pomdps using barrier certificates"
    - "understanding the role of adaptivity in machine teaching the case of version space learners"
    - "barrier certificates for assured machine teaching"
    - "privacy verification in pomdps via barrier certificates"
    - "machine teaching a new paradigm for building machine learning systems"
    - "safe learning of regions of attraction for uncertain nonlinear systems with gaussian processes"
    - "heuristic search value iteration for pomdps"

Connected through hyperedge HE-8 (induced by source paper "temporal difference variational auto encoder"):
  with members:
    - "stochastic adversarial video prediction"
    - "learning and querying fast generative models for reinforcement learning"
    - "deep variational reinforcement learning for pomdps"
    - "learning awareness models"
    - "adaptive skip intervals temporal abstraction for recurrent dynamical models"
    - "time agnostic prediction predicting predictable video frames"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "a distributional perspective on reinforcement learning"

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)