human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "unsupervised visual linguistic reference resolution in instructional videos":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "howto100m learning a text video embedding by watching hundred million narrated video clips")
  Member "multi modal imitation learning from unstructured demonstrations using generative adversarial nets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "machine learning approaches for motor learning a short review")
  Member "hindsight experience replay":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "generating descriptions with grounded and co referenced people":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "visual grounding in video for unsupervised word translation")
  Member "deep object centric representations for generalizable robot learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "good robot efficient reinforcement learning for multi step visual tasks with sim to real transfer")
  Member "learning complex dexterous manipulation with deep reinforcement learning and demonstrations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hierarchically decoupled imitation for morphological transfer")
  Member "schema networks zero shot transfer with a generative causal model of intuitive physics":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "neuroevolution of self interpretable agents")
  Member "collective robot reinforcement learning with distributed asynchronous guided policy search":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "rigid soft interactive learning for robust grasping")

Question: Please tell me which class the source paper 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)