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 "draw a recurrent neural network for image generation"):
  Abstract: This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural network architecture for image generation. DRAW networks combine a novel spatial attention mechanism that mimics the foveation of the human eye, with a sequential variational auto-encoding framework that allows for the iterative construction of complex images. The system substantially improves on the state of the art for generative models on MNIST, and, when trained on the Street View House Numbers dataset, it generates images that cannot be distinguished from real data with the naked eye.

Includes members:
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "neural variational inference and learning in belief networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "estimating gradients for discrete random variables by sampling without replacement")
  Member "neural turing machines":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "recurrent models of visual attention":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "see more know more unsupervised video object segmentation with co attention siamese networks")
  Member "multiple object recognition with visual attention":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "neuroevolution of self interpretable agents")
  Member "on learning where to look":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "pay attention to the activations a modular attention mechanism for fine grained image recognition")
  Member "attention for fine grained categorization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "pay attention to the activations a modular attention mechanism for fine grained image recognition")
  Member "sequence to sequence learning with neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")

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)