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 "super mario as a string platformer level generation via lstms"):
  Abstract: The procedural generation of video game levels has existed for at least 30 years, but only recently have machine learning approaches been used to generate levels without specifying the rules for generation. A number of these have looked at platformer levels as a sequence of characters and performed generation using Markov chains. In this paper we examine the use of Long Short-Term Memory recurrent neural networks (LSTMs) for the purpose of generating levels trained from a corpus of Super Mario Brothers levels. We analyze a number of different data representations and how the generated levels fit into the space of human authored Super Mario Brothers levels.

Includes members:
  Member "toward game level generation from gameplay videos":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "conceptual game expansion")
  Member "deep generative image models using a laplacian pyramid of adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "draw a recurrent neural network for image generation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "twostreamvan improving motion modeling in video generation")
  Member "going deeper with convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "translating videos to natural language using deep recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "learning to execute":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "curriculum by texture")
  Member "sequence to sequence learning with neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "speech recognition with deep recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "centrality graph convolutional networks for skeleton based action recognition")

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.NE(Neural and Evolutionary Computing)