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 "space unsupervised object oriented scene representation via spatial attention and decomposition"):
  Abstract: The ability to decompose complex multi-object scenes into meaningful abstractions like objects is fundamental to achieve higher-level cognition. Previous approaches for unsupervised object-oriented scene representation learning are either based on spatial-attention or scene-mixture approaches and limited in scalability which is a main obstacle towards modeling real-world scenes. In this paper, we propose a generative latent variable model, called SPACE, that provides a unified probabilistic modeling framework that combines the best of spatial-attention and scene-mixture approaches. SPACE can explicitly provide factorized object representations for foreground objects while also decomposing background segments of complex morphology. Previous models are good at either of these, but not both. SPACE also resolves the scalability problems of previous methods by incorporating parallel spatial-attention and thus is applicable to scenes with a large number of objects without performance degradations. We show through experiments on Atari and 3D-Rooms that SPACE achieves the above properties consistently in comparison to SPAIR, IODINE, and GENESIS. Results of our experiments can be found on our project website: this https URL

Includes members:
  Member "genesis generative scene inference and sampling with object centric latent representations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence")
  Member "exploiting spatial invariance for scalable unsupervised object tracking":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scalor generative world models with scalable object representations")
  Member "monet unsupervised scene decomposition and representation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence")
  Member "spatial broadcast decoder a simple architecture for learning disentangled representations in vaes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "high fidelity synthesis with disentangled representation")
  Member "multi object representation learning with iterative variational inference":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "language as a cognitive tool to imagine goals in curiosity driven exploration")
  Member "relational neural expectation maximization unsupervised discovery of objects and their interactions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "a simple neural network module for relational reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "neural expectation maximization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "scalor generative world models with scalable object representations")

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.LG(Machine Learning)