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: "a symbolic sat based algorithm for almost sure reachability with small strategies in pomdps"
  Abstract: POMDPs are standard models for probabilistic planning problems, where an agent interacts with an uncertain environment. We study the problem of almost-sure reachability, where given a set of target states, the question is to decide whether there is a policy to ensure that the target set is reached with probability 1 (almost-surely). While in general the problem is EXPTIME-complete, in many practical cases policies with a small amount of memory suffice. Moreover, the existing solution to the problem is explicit, which first requires to construct explicitly an exponential reduction to a belief-support MDP. In this work, we first study the existence of observation-stationary strategies, which is NP-complete, and then small-memory strategies. We present a symbolic algorithm by an efficient encoding to SAT and using a SAT solver for the problem. We report experimental results demonstrating the scalability of our symbolic (SAT-based) approach.

Connected through hyperedge HE-1 (induced by source paper "verifiable rnn based policies for pomdps under temporal logic constraints"):
  with members:
    - "interpreting and evaluating neural network robustness"
    - "representing formal languages a comparison between finite automata and recurrent neural networks"
    - "extracting automata from recurrent neural networks using queries and counterexamples"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "qualitatively characterizing neural network optimization problems"
    - "how to construct deep recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "counterexample driven synthesis for probabilistic program sketches"):
  with members:
    - "shepherding hordes of markov chains"
    - "structured synthesis for probabilistic systems"
    - "safety aware apprenticeship learning"
    - "a storm is coming a modern probabilistic model checker"
    - "parameter synthesis for markov models faster than ever"
    - "high level counterexamples for probabilistic automata"
    - "safety constrained reinforcement learning for mdps"
    - "optimal cost almost sure reachability in pomdps"

Connected through hyperedge HE-3 (induced by source paper "on the complexity of reachability in parametric markov decision processes"):
  with members:
    - "parameter synthesis for markov models"
    - "qualitative reachability for open interval markov chains"
    - "accelerated model checking of parametric markov chains"
    - "sea param exploring schedulers in parametric mdps"
    - "parametric markov chains pctl complexity and fraction free gaussian elimination"
    - "a storm is coming a modern probabilistic model checker"
    - "parameter synthesis for markov models faster than ever"
    - "accelerating parametric probabilistic verification"

Connected through hyperedge HE-4 (induced by source paper "shepherding hordes of markov chains"):
  with members:
    - "parameter independent strategies for pmdps via pomdps"
    - "reachability in augmented interval markov chains"
    - "a storm is coming a modern probabilistic model checker"
    - "optimal cost almost sure reachability in pomdps"

Connected through hyperedge HE-5 (induced by source paper "a correctness result for synthesizing plans with loops in stochastic domains"):
  with members:
    - "solving pomdps by searching the space of finite policies"

Connected through hyperedge HE-6 (induced by source paper "model free model based and general intelligence"):
  with members:
    - "generalized planning non deterministic abstractions and trajectory constraints"
    - "feature based aggregation and deep reinforcement learning a survey and some new implementations"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "planning with pixels in almost real time"
    - "features projections and representation change for generalized planning"
    - "deep learning a critical appraisal"
    - "learning generalized reactive policies using deep neural networks"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"

Connected through hyperedge HE-7 (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"
    - "optimal cost almost sure reachability in pomdps"
    - "pomdps under probabilistic semantics"

Connected through hyperedge HE-8 (induced by source paper "bounded policy synthesis for pomdps with safe reachability objectives"):
  with members:
    - "qualitative analysis of pomdps with temporal logic specifications for robotics applications"

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)