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: "what should i do now marrying reinforcement learning and symbolic planning"
  Abstract: Long-term planning poses a major difficulty to many reinforcement learning algorithms. This problem becomes even more pronounced in dynamic visual environments. In this work we propose Hierarchical Planning and Reinforcement Learning (HIP-RL), a method for merging the benefits and capabilities of Symbolic Planning with the learning abilities of Deep Reinforcement Learning. We apply HIPRL to the complex visual tasks of interactive question answering and visual semantic planning and achieve state-of-the-art results on three challenging datasets all while taking fewer steps at test time and training in fewer iterations. Sample results can be found at youtu.be/0TtWJ_0mPfI

Connected through hyperedge HE-1 (induced by source paper "from seeing to moving a survey on learning for visual indoor navigation vin"):
  with members:
    - "walking with mind mental imagery enhanced embodied qa"
    - "are we making real progress in simulated environments measuring the sim2real gap in embodied visual navigation"
    - "embodied question answering in photorealistic environments with point cloud perception"
    - "graph attention memory for visual navigation"
    - "vision language navigation with self supervised auxiliary reasoning tasks"
    - "multi target embodied question answering"
    - "learning your way without map or compass panoramic target driven visual navigation"
    - "integrating knowledge and reasoning in image understanding"

Connected through hyperedge HE-2 (induced by source paper "alfred a benchmark for interpreting grounded instructions for everyday tasks"):
  with members:
    - "vision and dialog navigation"
    - "tactical rewind self correction via backtracking in vision and language navigation"
    - "embodied question answering in photorealistic environments with point cloud perception"
    - "multi target embodied question answering"
    - "the regretful agent heuristic aided navigation through progress estimation"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "learning to navigate unseen environments back translation with environmental dropout"
    - "interactive visual grounding of referring expressions for human robot interaction"

Connected through hyperedge HE-3 (induced by source paper "desiderata for planning systems in general purpose service robots"):
  with members:
    - "improving grounded natural language understanding through human robot dialog"
    - "interaction and autonomy in robocup home and building wide intelligence"
    - "integrating task motion planning with reinforcement learning for robust decision making in mobile robots"
    - "balancing explicability and explanation in human aware planning"
    - "the strands project long term autonomy in everyday environments"
    - "plan explanations as model reconciliation moving beyond explanation as soliloquy"
    - "a refinement based architecture for knowledge representation and reasoning in robotics"
    - "plan explicability and predictability for robot task planning"

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.CV(Computer Vision and Pattern Recognition)