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 "investigating object compositionality in generative adversarial networks"):
  Abstract: Deep generative models seek to recover the process with which the observed data was generated. They may be used to synthesize new samples or to subsequently extract representations. Successful approaches in the domain of images are driven by several core inductive biases. However, a bias to account for the compositional way in which humans structure a visual scene in terms of objects has frequently been overlooked. In this work we investigate object compositionality as an inductive bias for Generative Adversarial Networks (GANs). We present a minimal modification of a standard generator to incorporate this inductive bias and find that it reliably learns to generate images as compositions of objects. Using this general design as a backbone, we then propose two useful extensions to incorporate dependencies among objects and background. We extensively evaluate our approach on several multi-object image datasets and highlight the merits of incorporating structure for representation learning purposes. In particular, we find that our structured GANs are better at generating multi-object images that are more faithful to the reference distribution. More so, we demonstrate how, by leveraging the structure of the learned generative process, it can be `inverted' to perform unsupervised instance segmentation. Compared to prior work on purely unsupervised object-centric image generation our results on CLEVR are state of the art.

Includes members:
  Member "generating multiple objects at spatially distinct locations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "blockgan learning 3d object aware scene representations from unlabelled images")
  Member "emergence of object segmentation in perturbed generative models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "blockgan learning 3d object aware scene representations from unlabelled images")
  Member "multi object representation learning with iterative variational inference":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "language as a cognitive tool to imagine goals in curiosity driven exploration")
  Member "unsupervised minimax adversarial curiosity generative adversarial networks and predictability minimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "action for better prediction")
  Member "unsupervised object segmentation by redrawing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "blockgan learning 3d object aware scene representations from unlabelled images")
  Member "object discovery with a copy pasting gan":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multiple identity image attacks against face based identity verification")
  Member "relational neural expectation maximization unsupervised discovery of objects and their interactions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "the gan landscape losses architectures regularization and normalization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")

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)