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: "tagger deep unsupervised perceptual grouping"
  Abstract: We present a framework for efficient perceptual inference that explicitly reasons about the segmentation of its inputs and features. Rather than being trained for any specific segmentation, our framework learns the grouping process in an unsupervised manner or alongside any supervised task. By enriching the representations of a neural network, we enable it to group the representations of different objects in an iterative manner. By allowing the system to amortize the iterative inference of the groupings, we achieve very fast convergence. In contrast to many other recently proposed methods for addressing multi-object scenes, our system does not assume the inputs to be images and can therefore directly handle other modalities. For multi-digit classification of very cluttered images that require texture segmentation, our method offers improved classification performance over convolutional networks despite being fully connected. Furthermore, we observe that our system greatly improves on the semi-supervised result of a baseline Ladder network on our dataset, indicating that segmentation can also improve sample efficiency.

Connected through hyperedge HE-1 (induced by source paper "an explicit local and global representation disentanglement framework with applications in deep clustering and unsupervised object detection"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "implicit autoencoders"
    - "explicit disentanglement of appearance and perspective in generative models"
    - "an explicitly relational neural network architecture"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "isolating sources of disentanglement in variational autoencoders"
    - "learning latent representations in neural networks for clustering through pseudo supervision and graph based activity regularization"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"

Connected through hyperedge HE-2 (induced by source paper "towards unsupervised learning of generative models for 3d controllable image synthesis"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "genesis generative scene inference and sampling with object centric latent representations"
    - "a layer based sequential framework for scene generation with gans"
    - "monet unsupervised scene decomposition and representation"
    - "singan learning a generative model from a single natural image"
    - "escaping plato s cave 3d shape from adversarial rendering"
    - "layoutgan generating graphic layouts with wireframe discriminators"
    - "structured3d a large photo realistic dataset for structured 3d modeling"

Connected through hyperedge HE-3 (induced by source paper "genesis generative scene inference and sampling with object centric latent representations"):
  with members:
    - "emergence of object segmentation in perturbed generative models"
    - "monet unsupervised scene decomposition and representation"
    - "are disentangled representations helpful for abstract visual reasoning"
    - "cobra data efficient model based rl through unsupervised object discovery and curiosity driven exploration"
    - "spatial broadcast decoder a simple architecture for learning disentangled representations in vaes"
    - "unsupervised discovery of parts structure and dynamics"
    - "multi object representation learning with iterative variational inference"
    - "unsupervised object segmentation by redrawing"

Connected through hyperedge HE-4 (induced by source paper "interpreting and understanding graph convolutional neural network using gradient based attribution methods"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "relational inductive biases deep learning and graph networks"
    - "graph convolutional networks for named entity recognition"
    - "a simple neural network module for relational reasoning"
    - "axiomatic attribution for deep networks"
    - "learning important features through propagating activation differences"
    - "towards better understanding of gradient based attribution methods for deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "multi object representation learning with iterative variational inference"):
  with members:
    - "monet unsupervised scene decomposition and representation"
    - "spatial broadcast decoder a simple architecture for learning disentangled representations in vaes"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "iterative amortized inference"
    - "learning deep disentangled embeddings with the f statistic loss"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "yolov3 an incremental improvement"
    - "towards a definition of disentangled representations"

Connected through hyperedge HE-6 (induced by source paper "r sqair relational sequential attend infer repeat"):
  with members:
    - "monet unsupervised scene decomposition and representation"
    - "a perspective on objects and systematic generalization in model based rl"
    - "cobra data efficient model based rl through unsupervised object discovery and curiosity driven exploration"
    - "multi object representation learning with iterative variational inference"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "relational recurrent neural networks"
    - "relational inductive biases deep learning and graph networks"

Connected through hyperedge HE-7 (induced by source paper "spatial mixture models with learnable deep priors for perceptual grouping"):
  with members:
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "recurrent ladder networks"
    - "neural message passing for quantum chemistry"
    - "neural expectation maximization"
    - "building machines that learn and think like people"
    - "semi supervised learning with ladder networks"
    - "binding via reconstruction clustering"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "learning to decompose and disentangle representations for video prediction"):
  with members:
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "visual robot task planning"
    - "decomposing motion and content for natural video sequence prediction"
    - "learning to generate long term future via hierarchical prediction"
    - "folded recurrent neural networks for future video prediction"
    - "unsupervised learning of disentangled representations from video"
    - "neural expectation maximization"

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)