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: "reward learning from narrated demonstrations"
  Abstract: Humans effortlessly "program" one another by communicating goals and desires in natural language. In contrast, humans program robotic behaviours by indicating desired object locations and poses to be achieved, by providing RGB images of goal configurations, or supplying a demonstration to be imitated. None of these methods generalize across environment variations, and they convey the goal in awkward technical terms. This work proposes joint learning of natural language grounding and instructable behavioural policies reinforced by perceptual detectors of natural language expressions, grounded to the sensory inputs of the robotic agent. Our supervision is narrated visual demonstrations(NVD), which are visual demonstrations paired with verbal narration (as opposed to being silent). We introduce a dataset of NVD where teachers perform activities while describing them in detail. We map the teachers' descriptions to perceptual reward detectors, and use them to train corresponding behavioural policies in simulation.We empirically show that our instructable agents (i) learn visual reward detectors using a small number of examples by exploiting hard negative mined configurations from demonstration dynamics, (ii) develop pick-and place policies using learned visual reward detectors, (iii) benefit from object-factorized state representations that mimic the syntactic structure of natural language goal expressions, and (iv) can execute behaviours that involve novel objects in novel locations at test time, instructed by natural language.

Connected through hyperedge HE-1 (induced by source paper "a model of fast concept inference with object factorized cognitive programs"):
  with members:
    - "beyond imitation zero shot task transfer on robots by learning concepts as cognitive programs"

Connected through hyperedge HE-2 (induced by source paper "grounding human to vehicle advice for self driving vehicles"):
  with members:
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "textual explanations for self driving vehicles"
    - "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"
    - "interpretable learning for self driving cars by visualizing causal attention"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "teaching machines to describe images via natural language feedback"
    - "end to end learning for self driving cars"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-3 (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"
    - "accurately and efficiently interpreting human robot instructions of varying granularities"

Connected through hyperedge HE-4 (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"
    - "accurately and efficiently interpreting human robot instructions of varying granularities"
    - "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-5 (induced by source paper "few shot goal inference for visuomotor learning and planning"):
  with members:
    - "universal planning networks"
    - "multi task maximum entropy inverse reinforcement learning"
    - "learning a prior over intent via meta inverse reinforcement learning"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "self supervised visual planning with temporal skip connections"
    - "one shot visual imitation learning via meta learning"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "collective robot reinforcement learning with distributed asynchronous guided policy search"

Connected through hyperedge HE-6 (induced by source paper "beyond imitation zero shot task transfer on robots by learning concepts as cognitive programs"):
  with members:
    - "bridging cognitive programs and machine learning"
    - "synthetically trained neural networks for learning human readable plans from real world demonstrations"
    - "neural task graphs generalizing to unseen tasks from a single video demonstration"
    - "deep learning a critical appraisal"
    - "synthesizing programs for images using reinforced adversarial learning"
    - "inferring and executing programs for visual reasoning"
    - "star rt visual attention for real time video game playing"
    - "one shot visual imitation learning via meta learning"

Connected through hyperedge HE-7 (induced by source paper "variational inverse control with events a general framework for data driven reward definition"):
  with members:
    - "sim to real transfer of robotic control with dynamics randomization"
    - "equivalence between policy gradients and soft q learning"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "bridging the gap between value and policy based reinforcement learning"
    - "reinforcement learning with deep energy based policies"
    - "inverse reward design"
    - "generative adversarial imitation learning"
    - "concrete problems in ai safety"

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)