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: "accurately and efficiently interpreting human robot instructions of varying granularities"
  Abstract: Humans can ground natural language commands to tasks at both abstract and fine-grained levels of specificity. For instance, a human forklift operator can be instructed to perform a high-level action, like "grab a pallet" or a low-level action like "tilt back a little bit." While robots are also capable of grounding language commands to tasks, previous methods implicitly assume that all commands and tasks reside at a single, fixed level of abstraction. Additionally, methods that do not use multiple levels of abstraction encounter inefficient planning and execution times as they solve tasks at a single level of abstraction with large, intractable state-action spaces closely resembling real world complexity. In this work, by grounding commands to all the tasks or subtasks available in a hierarchical planning framework, we arrive at a model capable of interpreting language at multiple levels of specificity ranging from coarse to more granular. We show that the accuracy of the grounding procedure is improved when simultaneously inferring the degree of abstraction in language used to communicate the task. Leveraging hierarchy also improves efficiency: our proposed approach enables a robot to respond to a command within one second on 90% of our tasks, while baselines take over twenty seconds on half the tasks. Finally, we demonstrate that a real, physical robot can ground commands at multiple levels of abstraction allowing it to efficiently plan different subtasks within the same planning hierarchy.

Connected through hyperedge HE-1 (induced by source paper "using natural language for reward shaping in reinforcement learning"):
  with members:
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "learning to understand goal specifications by modelling reward"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "dcn mixed objective and deep residual coattention for question answering"
    - "mapping instructions and visual observations to actions with reinforcement learning"
    - "the atari grand challenge dataset"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-2 (induced by source paper "a narration based reward shaping approach using grounded natural language commands"):
  with members:
    - "from language to goals inverse reinforcement learning for vision based instruction following"
    - "grounding natural language commands to starcraft ii game states for narration guided reinforcement learning"
    - "on reinforcement learning for full length game of starcraft"
    - "mapping instructions to actions in 3d environments with visual goal prediction"
    - "follownet robot navigation by following natural language directions with deep reinforcement learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "following high level navigation instructions on a simulated quadcopter with imitation learning"
    - "reward learning from narrated demonstrations"

Connected through hyperedge HE-3 (induced by source paper "grounding natural language commands to starcraft ii game states for narration guided reinforcement learning"):
  with members:
    - "efficiently combining human demonstrations and interventions for safe training of autonomous systems in real time"
    - "on reinforcement learning for full length game of starcraft"
    - "follownet robot navigation by following natural language directions with deep reinforcement learning"
    - "following high level navigation instructions on a simulated quadcopter with imitation learning"
    - "reward learning from narrated demonstrations"
    - "starcraft ii a new challenge for reinforcement learning"
    - "deep tamer interactive agent shaping in high dimensional state spaces"
    - "listen attend and walk neural mapping of navigational instructions to action sequences"

Connected through hyperedge HE-4 (induced by source paper "planning with state abstractions for non markovian task specifications"):
  with members:
    - "evaluating compositionality in sentence embeddings"
    - "still not systematic after all these years on the compositional skills of sequence to sequence recurrent networks"
    - "interpretable apprenticeship learning with temporal logic specifications"
    - "six challenges for neural machine translation"
    - "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "constructing abstraction hierarchies using a skill symbol loop"
    - "adam a method for stochastic optimization"

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)