human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "monet unsupervised scene decomposition and representation"
  Abstract: The ability to decompose scenes in terms of abstract building blocks is crucial for general intelligence. Where those basic building blocks share meaningful properties, interactions and other regularities across scenes, such decompositions can simplify reasoning and facilitate imagination of novel scenarios. In particular, representing perceptual observations in terms of entities should improve data efficiency and transfer performance on a wide range of tasks. Thus we need models capable of discovering useful decompositions of scenes by identifying units with such regularities and representing them in a common format. To address this problem, we have developed the Multi-Object Network (MONet). In this model, a VAE is trained end-to-end together with a recurrent attention network -- in a purely unsupervised manner -- to provide attention masks around, and reconstructions of, regions of images. We show that this model is capable of learning to decompose and represent challenging 3D scenes into semantically meaningful components, such as objects and background elements.

Connected through hyperedge HE-1 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence"):
  with members:
    - "memo a deep network for flexible combination of episodic memories"
    - "towards a human like open domain chatbot"
    - "learning higher order logic programs"
    - "solving rubik s cube with a robot hand"
    - "genesis generative scene inference and sampling with object centric latent representations"
    - "on the measure of intelligence"
    - "entity abstraction in visual model based reinforcement learning"
    - "emergent systematic generalization in a situated agent"

Connected through hyperedge HE-2 (induced by source paper "language as a cognitive tool to imagine goals in curiosity driven exploration"):
  with members:
    - "emergent systematic generalization in a situated agent"
    - "skew fit state covering self supervised reinforcement learning"
    - "language as an abstraction for hierarchical deep reinforcement learning"
    - "extending machine language models toward human level language understanding"
    - "using natural language for reward shaping in reinforcement learning"
    - "on the limitations of representing functions on sets"
    - "from language to goals inverse reinforcement learning for vision based instruction following"
    - "multi object representation learning with iterative variational inference"

Connected through hyperedge HE-3 (induced by source paper "sviro synthetic vehicle interior rear seat occupancy dataset and benchmark"):
  with members:
    - "how much real data do we actually need analyzing object detection performance using synthetic and real data"
    - "eurocity persons a novel benchmark for person detection in traffic scenes"
    - "moment matching for multi source domain adaptation"
    - "syn2real a new benchmark forsynthetic to real visual domain adaptation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "museum exhibit identification challenge for domain adaptation and beyond"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"

Connected through hyperedge HE-4 (induced by source paper "product kanerva machines factorized bayesian memory"):
  with members:
    - "memo a deep network for flexible combination of episodic memories"
    - "generalization of reinforcement learners with working and episodic memory"
    - "mcp learning composable hierarchical control with multiplicative compositional policies"
    - "metalearned neural memory"
    - "recurrent independent mechanisms"
    - "spatial broadcast decoder a simple architecture for learning disentangled representations in vaes"
    - "shaping belief states with generative environment models for rl"
    - "optimizing agent behavior over long time scales by transporting value"

Connected through hyperedge HE-5 (induced by source paper "instance separation emerges from inpainting"):
  with members:
    - "learning correspondence from the cycle consistency of time"
    - "learning pyramid context encoder network for high quality image inpainting"
    - "selfie self supervised pretraining for image embedding"
    - "structureflow image inpainting via structure aware appearance flow"
    - "unsupervised object segmentation by redrawing"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "noise2void learning denoising from single noisy images"
    - "seigan towards compositional image generation by simultaneously learning to segment enhance and inpaint"

Connected through hyperedge HE-6 (induced by source paper "learning visuomotor policies for aerial navigation using cross modal representations"):
  with members:
    - "deep drone racing from simulation to reality with domain randomization"
    - "generalization through simulation integrating simulated and real data into deep reinforcement learning for vision based autonomous flight"
    - "towards a robust aerial cinematography platform localizing and tracking moving targets in unstructured environments"
    - "deep drone racing learning agile flight in dynamic environments"
    - "an algorithmic perspective on imitation learning"
    - "learning latent dynamics for planning from pixels"
    - "world models"
    - "state representation learning for control an overview"

Connected through hyperedge HE-7 (induced by source paper "space unsupervised object oriented scene representation via spatial attention and decomposition"):
  with members:
    - "genesis generative scene inference and sampling with object centric latent representations"
    - "exploiting spatial invariance for scalable unsupervised object tracking"
    - "spatial broadcast decoder a simple architecture for learning disentangled representations in vaes"
    - "multi object representation learning with iterative variational inference"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "a simple neural network module for relational reasoning"
    - "neural expectation maximization"
    - "continuously differentiable exponential linear units"

Connected through hyperedge HE-8 (induced by source paper "scalor generative world models with scalable object representations"):
  with members:
    - "space unsupervised object oriented scene representation via spatial attention and decomposition"
    - "genesis generative scene inference and sampling with object centric latent representations"
    - "exploiting spatial invariance for scalable unsupervised object tracking"
    - "multi object representation learning with iterative variational inference"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "learning to decompose and disentangle representations for video prediction"
    - "tracking by animation unsupervised learning of multi object attentive trackers"
    - "hierarchical attentive recurrent tracking"

Question: Please tell me which class the center node 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)