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: "hyp despot a hybrid parallel algorithm for online planning under uncertainty"
  Abstract: Planning under uncertainty is critical for robust robot performance in uncertain, dynamic environments, but it incurs high computational cost. State-of-the-art online search algorithms, such as DESPOT, have vastly improved the computational efficiency of planning under uncertainty and made it a valuable tool for robotics in practice. This work takes one step further by leveraging both CPU and GPU parallelization in order to achieve near real-time online planning performance for complex tasks with large state, action, and observation spaces. Specifically, we propose Hybrid Parallel DESPOT (HyP-DESPOT), a massively parallel online planning algorithm that integrates CPU and GPU parallelism in a multi-level scheme. It performs parallel DESPOT tree search by simultaneously traversing multiple independent paths using multi-core CPUs and performs parallel Monte-Carlo simulations at the leaf nodes of the search tree using GPUs. Experimental results show that HyP-DESPOT speeds up online planning by up to several hundred times, compared with the original DESPOT algorithm, in several challenging robotic tasks in simulation.

Connected through hyperedge HE-1 (induced by source paper "efficient uncertainty aware decision making for automated driving using guided branching"):
  with members:
    - "predicting vehicle behaviors over an extended horizon using behavior interaction network"
    - "safe trajectory generation for complex urban environments using spatio temporal semantic corridor"
    - "on a formal model of safe and scalable self driving cars"
    - "despot online pomdp planning with regularization"
    - "online planning algorithms for pomdps"

Connected through hyperedge HE-2 (induced by source paper "what should i do now marrying reinforcement learning and symbolic planning"):
  with members:
    - "fusion volumetric object level slam"
    - "gibson env real world perception for embodied agents"
    - "chalet cornell house agent learning environment"
    - "on evaluation of embodied navigation agents"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "shifting the baseline single modality performance on visual navigation qa"
    - "neural modular control for embodied question answering"
    - "gated attention architectures for task oriented language grounding"

Connected through hyperedge HE-3 (induced by source paper "lets drive driving in a crowd by learning from tree search"):
  with members:
    - "porca modeling and planning for autonomous driving among many pedestrians"
    - "gated path planning networks"
    - "crowdmove autonomous mapless navigation in crowded scenarios"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "thinking fast and slow with deep learning and tree search"
    - "socially aware motion planning with deep reinforcement learning"
    - "despot online pomdp planning with regularization"
    - "value iteration networks"

Connected through hyperedge HE-4 (induced by source paper "summit a simulator for urban driving in massive mixed traffic"):
  with members:
    - "lets drive driving in a crowd by learning from tree search"
    - "gamma a general agent motion prediction model for autonomous driving"
    - "learning dexterous in hand manipulation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "porca modeling and planning for autonomous driving among many pedestrians"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "carla an open urban driving simulator"
    - "playing for benchmarks"

Connected through hyperedge HE-5 (induced by source paper "poddp partially observable differential dynamic programming for latent belief space planning"):
  with members:
    - "hierarchical game theoretic planning for autonomous vehicles"
    - "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-6 (induced by source paper "stochastic 2 d motion planning with a pomdp framework"):
  with members:
    - "point based pomdp algorithms improved analysis and implementation"
    - "heuristic search value iteration for pomdps"
    - "online planning algorithms for pomdps"
    - "speeding up the convergence of value iteration in partially observable markov decision processes"
    - "value function approximations for partially observable markov decision processes"

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)