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 "learning to sketch with deep q networks and demonstrated strokes"):
  Abstract: Doodling is a useful and common intelligent skill that people can learn and master. In this work, we propose a two-stage learning framework to teach a machine to doodle in a simulated painting environment via Stroke Demonstration and deep Q-learning (SDQ). The developed system, Doodle-SDQ, generates a sequence of pen actions to reproduce a reference drawing and mimics the behavior of human painters. In the first stage, it learns to draw simple strokes by imitating in supervised fashion from a set of strokeaction pairs collected from artist paintings. In the second stage, it is challenged to draw real and more complex doodles without ground truth actions; thus, it is trained with Qlearning. Our experiments confirm that (1) doodling can be learned without direct stepby- step action supervision and (2) pretraining with stroke demonstration via supervised learning is important to improve performance. We further show that Doodle-SDQ is effective at producing plausible drawings in different media types, including sketch and watercolor.

Includes members:
  Member "pre training neural networks with human demonstrations for deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "exploration efficient deep reinforcement learning with demonstration guidance for robot control")
  Member "synthesizing programs for images using reinforced adversarial learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "painting many pasts synthesizing time lapse videos of paintings")
  Member "can creative adversarial networks generating art by learning about styles and deviating from style norms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "music generation by deep learning challenges and directions")
  Member "a neural representation of sketch drawings":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep self supervised representation learning for free hand sketch")
  Member "generating videos with scene dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "prioritized experience replay":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "deep reinforcement learning in large discrete action spaces":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "i love your chain mail making knights smile in a fantasy game world open domain goal orientated dialogue agents")
  Member "deep generative image models using a laplacian pyramid of adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "transformation based adversarial video prediction on large scale data")

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)