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: "incremental pruning a simple fast exact method for partially observable markov decision processes"
  Abstract: Most exact algorithms for general partially observable Markov decision processes (POMDPs) use a form of dynamic programming in which a piecewise-linear and convex representation of one value function is transformed into another. We examine variations of the "incremental pruning" method for solving this problem and compare them to earlier algorithms from theoretical and empirical perspectives. We find that incremental pruning is presently the most efficient exact method for solving POMDPs.

Connected through hyperedge HE-1 (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-2 (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"
    - "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"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "scenario based verification of uncertain mdps"):
  with members:
    - "parameter synthesis for markov models"
    - "parameter independent strategies for pmdps via pomdps"
    - "incremental verification of parametric and reconfigurable markov chains"
    - "sequential convex programming for the efficient verification of parametric mdps"
    - "a storm is coming a modern probabilistic model checker"
    - "multi objective approaches to markov decision processes with uncertain transition parameters"
    - "parameter synthesis for markov models faster than ever"

Connected through hyperedge HE-4 (induced by source paper "sparse tree search optimality guarantees in pomdps with continuous observation spaces"):
  with members:
    - "policy optimization via importance sampling"
    - "the value of inferring the internal state of traffic participants for autonomous freeway driving"
    - "online algorithms for pomdps with continuous state action and observation spaces"
    - "approximate planning for factored pomdps using belief state simplification"

Connected through hyperedge HE-5 (induced by source paper "parameter independent strategies for pmdps via pomdps"):
  with members:
    - "sea param exploring schedulers in parametric mdps"
    - "sequential convex programming for the efficient verification of parametric mdps"
    - "automated experiment design for data efficient verification of parametric markov decision processes"
    - "a storm is coming a modern probabilistic model checker"
    - "ares adaptive receding horizon synthesis of optimal plans"
    - "parameter synthesis for markov models faster than ever"
    - "a symbolic sat based algorithm for almost sure reachability with small strategies in pomdps"
    - "optimal cost almost sure reachability in pomdps"

Connected through hyperedge HE-6 (induced by source paper "pilot beam sequence design for channel estimation in millimeter wave mimo systems a pomdp framework"):
  with members:
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "millimeter wave mimo channel tracking systems"
    - "point based pomdp algorithms improved analysis and implementation"

Connected through hyperedge HE-7 (induced by source paper "influence optimistic local values for multiagent planning extended version"):
  with members:
    - "maa a heuristic search algorithm for solving decentralized pomdps"
    - "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 "training beam sequence design for millimeter wave mimo systems a pomdp framework"):
  with members:
    - "pilot beam sequence design for channel estimation in millimeter wave mimo systems a pomdp framework"
    - "pilot signal design for massive mimo systems a received signal to noise ratio based approach"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "pilot beam pattern design for channel estimation in massive mimo systems"
    - "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory"
    - "millimeter wave mimo channel tracking systems"
    - "millimeter wave beamforming for wireless backhaul and access in small cell networks"
    - "point based pomdp algorithms improved analysis and implementation"

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)