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: "the value of inferring the internal state of traffic participants for autonomous freeway driving"
  Abstract: Safe interaction with human drivers is one of the primary challenges for autonomous vehicles. In order to plan driving maneuvers effectively, the vehicle's control system must infer and predict how humans will behave based on their latent internal state (e.g., intentions and aggressiveness). This research uses a simple model for human behavior with unknown parameters that make up the internal states of the traffic participants and presents a method for quantifying the value of estimating these states and planning with their uncertainty explicitly modeled. An upper performance bound is established by an omniscient Monte Carlo Tree Search (MCTS) planner that has perfect knowledge of the internal states. A baseline lower bound is established by planning with MCTS assuming that all drivers have the same internal state. MCTS variants are then used to solve a partially observable Markov decision process (POMDP) that models the internal state uncertainty to determine whether inferring the internal state offers an advantage over the baseline. Applying this method to a freeway lane changing scenario reveals that there is a significant performance gap between the upper bound and baseline. POMDP planning techniques come close to closing this gap, especially when important hidden model parameters are correlated with measurable parameters.

Connected through hyperedge HE-1 (induced by source paper "inference based strategy alignment for general sum differential games"):
  with members:
    - "on gradient based learning in continuous games"
    - "cooperation aware reinforcement learning for merging in dense traffic"
    - "on finding local nash equilibria and only local nash equilibria in zero sum games"
    - "on the convergence of gradient based learning in continuous games"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "hierarchical game theoretic planning for autonomous vehicles"
    - "a scalable framework for real time multi robot multi human collision avoidance"
    - "multimodal probabilistic model based planning for human robot interaction"

Connected through hyperedge HE-2 (induced by source paper "social attention for autonomous decision making in dense traffic"):
  with members:
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "deep imitative models for flexible inference planning and control"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "elements of effective deep reinforcement learning towards tactical driving decision making"
    - "learning to drive in a day"
    - "deeptraffic crowdsourced hyperparameter tuning of deep reinforcement learning systems for multi agent dense traffic navigation"
    - "recurrent world models facilitate policy evolution"

Connected through hyperedge HE-3 (induced by source paper "reinforcement learning with probabilistic guarantees for autonomous driving"):
  with members:
    - "bounded policy synthesis for pomdps with safe reachability objectives"
    - "shielded decision making in mdps"
    - "utility decomposition with deep corrections for scalable planning under uncertainty"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "safe reinforcement learning via shielding"
    - "prioritized experience replay"
    - "reinforcement learning a survey"

Connected through hyperedge HE-4 (induced by source paper "adaptive stress testing for autonomous vehicles"):
  with members:
    - "accelerated evaluation of automated vehicles safety in lane change scenarios based on importance sampling techniques"
    - "deep stochastic radar models"
    - "on a formal model of safe and scalable self driving cars"
    - "evaluation of automated vehicles in the frontal cut in scenario an enhanced approach using piecewise mixture models"
    - "benchmarking deep reinforcement learning for continuous control"
    - "high dimensional continuous control using generalized advantage estimation"
    - "trust region policy optimization"

Connected through hyperedge HE-5 (induced by source paper "poddp partially observable differential dynamic programming for latent belief space planning"):
  with members:
    - "hyp despot a hybrid parallel algorithm for online planning under uncertainty"
    - "hierarchical game theoretic planning for autonomous vehicles"
    - "deep variational reinforcement learning for pomdps"
    - "despot online pomdp planning with regularization"
    - "pomdp lite for robust robot planning under uncertainty"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "a scalable method for solving high dimensional continuous pomdps using local approximation"

Connected through hyperedge HE-6 (induced by source paper "information guided robotic maximum seek and sample in partially observable continuous environments"):
  with members:
    - "multi modal active perception for information gathering in science missions"
    - "no regret replanning under uncertainty"
    - "parallel gaussian process optimization with upper confidence bound and pure exploration"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"

Connected through hyperedge HE-7 (induced by source paper "sparse tree search optimality guarantees in pomdps with continuous observation spaces"):
  with members:
    - "policy optimization via importance sampling"
    - "online algorithms for pomdps with continuous state action and observation spaces"
    - "approximate planning for factored pomdps using belief state simplification"
    - "incremental pruning a simple fast exact method for partially observable markov decision processes"

Connected through hyperedge HE-8 (induced by source paper "real time prediction of intermediate horizon automotive collision risk"):
  with members:
    - "simultaneous policy learning and latent state inference for imitating driver behavior"
    - "accelerated evaluation of automated vehicles safety in lane change scenarios based on importance sampling techniques"
    - "imitating driver behavior with generative adversarial networks"
    - "unified deep supervised domain adaptation and generalization"
    - "learning invariant feature spaces to transfer skills with reinforcement learning"
    - "sim to real robot learning from pixels with progressive nets"
    - "domain separation networks"
    - "a reduction of imitation learning and structured prediction to no regret online 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)