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: "hierarchical game theoretic planning for autonomous vehicles"
  Abstract: The actions of an autonomous vehicle on the road affect and are affected by those of other drivers, whether overtaking, negotiating a merge, or avoiding an accident. This mutual dependence, best captured by dynamic game theory, creates a strong coupling between the vehicle's planning and its predictions of other drivers' behavior, and constitutes an open problem with direct implications on the safety and viability of autonomous driving technology. Unfortunately, dynamic games are too computationally demanding to meet the real-time constraints of autonomous driving in its continuous state and action space. In this paper, we introduce a novel game-theoretic trajectory planning algorithm for autonomous driving, that enables real-time performance by hierarchically decomposing the underlying dynamic game into a long-horizon "strategic" game with simplified dynamics and full information structure, and a short-horizon "tactical" game with full dynamics and a simplified information structure. The value of the strategic game is used to guide the tactical planning, implicitly extending the planning horizon, pushing the local trajectory optimization closer to global solutions, and, most importantly, quantitatively accounting for the autonomous vehicle and the human driver's ability and incentives to influence each other. In addition, our approach admits non-deterministic models of human decision-making, rather than relying on perfectly rational predictions. Our results showcase richer, safer, and more effective autonomous behavior in comparison to existing techniques.

Connected through hyperedge HE-1 (induced by source paper "sequential forecasting of 100 000 points"):
  with members:
    - "joint 3d tracking and forecasting with graph neural network and diversity sampling"
    - "conditional invertible flow for point cloud generation"
    - "grip graph based interaction aware trajectory prediction"
    - "probabilistic reconstruction networks for 3d shape inference from a single image"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "human motion trajectory prediction a survey"
    - "pixor real time 3d object detection from point clouds"
    - "a baseline for 3d multi object tracking"

Connected through hyperedge HE-2 (induced by source paper "mixed strategies for robust optimization of unknown objectives"):
  with members:
    - "cooperation aware reinforcement learning for merging in dense traffic"
    - "bayesian optimisation under uncertain inputs"
    - "no regret learning in unknown games with correlated payoffs"
    - "safe intersection management for mixed transportation systems with human driven and autonomous vehicles"
    - "distributionally robust submodular maximization"
    - "practical bayesian optimization in the presence of outliers"
    - "robust optimization for non convex objectives"
    - "on kernelized multi armed bandits"

Connected through hyperedge HE-3 (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"
    - "a scalable framework for real time multi robot multi human collision avoidance"
    - "the value of inferring the internal state of traffic participants for autonomous freeway driving"
    - "multimodal probabilistic model based planning for human robot interaction"

Connected through hyperedge HE-4 (induced by source paper "precog prediction conditioned on goals in visual multi agent settings"):
  with members:
    - "stochastic prediction of multi agent interactions from partial observations"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "generative hybrid representations for activity forecasting with no regret learning"
    - "an algorithmic perspective on imitation learning"
    - "deep imitative models for flexible inference planning and control"
    - "generative modeling of multimodal multi human behavior"
    - "generative multi agent behavioral cloning"

Connected through hyperedge HE-5 (induced by source paper "convergence of learning dynamics in stackelberg games"):
  with members:
    - "on gradient based learning in continuous games"
    - "what is local optimality in nonconvex nonconcave minimax optimization"
    - "on gradient descent ascent for nonconvex concave minimax problems"
    - "on finding local nash equilibria and only local nash equilibria in zero sum games"
    - "a closer look at the optimization landscapes of generative adversarial networks"
    - "on the convergence of gradient based learning in continuous games"
    - "the mechanics of n player differentiable games"
    - "goal inference improves objective and perceived performance in human robot collaboration"

Connected through hyperedge HE-6 (induced by source paper "game theoretic modeling of traffic in unsignalized intersection network for autonomous vehicle control verification and validation"):
  with members:
    - "a game theoretic approach to decision making for multiple vehicles at roundabout"
    - "game theoretic modeling of multi vehicle interactions at uncontrolled intersections"
    - "decision making for autonomous vehicles at unsignalized intersection in presence of malicious vehicles"
    - "using control synthesis to generate corner cases a case study on autonomous driving"
    - "adaptive game theoretic decision making for autonomous vehicle control at roundabouts"
    - "carla an open urban driving simulator"
    - "belief state planning for autonomously navigating urban intersections"
    - "navigating intersections with autonomous vehicles using deep reinforcement learning"

Connected through hyperedge HE-7 (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"
    - "deep variational reinforcement learning for pomdps"
    - "the value of inferring the internal state of traffic participants for autonomous freeway driving"
    - "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-8 (induced by source paper "cooperation aware reinforcement learning for merging in dense traffic"):
  with members:
    - "interaction aware decision making with adaptive strategies under merging scenarios"
    - "safe reinforcement learning with scene decomposition for navigating complex urban environments"
    - "collaborative planning for mixed autonomy lane merging"
    - "multimodal probabilistic model based planning for human robot interaction"
    - "prioritized experience replay"

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.RO(Robotics)