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: "binding via reconstruction clustering"
  Abstract: Disentangled distributed representations of data are desirable for machine learning, since they are more expressive and can generalize from fewer examples. However, for complex data, the distributed representations of multiple objects present in the same input can interfere and lead to ambiguities, which is commonly referred to as the binding problem. We argue for the importance of the binding problem to the field of representation learning, and develop a probabilistic framework that explicitly models inputs as a composition of multiple objects. We propose an unsupervised algorithm that uses denoising autoencoders to dynamically bind features together in multi-object inputs through an Expectation-Maximization-like clustering process. The effectiveness of this method is demonstrated on artificially generated datasets of binary images, showing that it can even generalize to bind together new objects never seen by the autoencoder during training.

Connected through hyperedge HE-1 (induced by source paper "entity abstraction in visual model based reinforcement learning"):
  with members:
    - "combining physical simulators and object based networks for control"
    - "monet unsupervised scene decomposition and representation"
    - "task agnostic dynamics priors for deep reinforcement learning"
    - "unsupervised learning of object keypoints for perception and control"
    - "object oriented dynamics learning through multi level abstraction"
    - "cobra data efficient model based rl through unsupervised object discovery and curiosity driven exploration"
    - "deep set prediction networks"
    - "unsupervised discovery of parts structure and dynamics"

Connected through hyperedge HE-2 (induced by source paper "holodetect few shot learning for error detection"):
  with members:
    - "autoaugment learning augmentation policies from data"
    - "a formal framework for probabilistic unclean databases"
    - "on calibration of modern neural networks"
    - "snorkel rapid training data creation with weak supervision"
    - "holoclean holistic data repairs with probabilistic inference"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "wide deep learning for recommender systems"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-3 (induced by source paper "the relational processing limits of classic and contemporary neural network models of language processing"):
  with members:
    - "learning to make analogies by contrasting abstract relational structure"
    - "relational inductive biases deep learning and graph networks"
    - "deep learning a critical appraisal"
    - "rearranging the familiar testing compositional generalization in recurrent networks"
    - "a simple neural network module for relational reasoning"
    - "neural symbolic learning and reasoning a survey and interpretation"
    - "adversarial examples for evaluating reading comprehension systems"
    - "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks"

Connected through hyperedge HE-4 (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"
    - "tagger deep unsupervised perceptual grouping"
    - "semi supervised learning with ladder networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "invariant information clustering for unsupervised image classification and segmentation"):
  with members:
    - "deep clustering for unsupervised learning of visual features"
    - "mine mutual information neural estimation"
    - "deeppermnet visual permutation learning"
    - "scaling the scattering transform deep hybrid networks"
    - "learning to segment every thing"
    - "unsupervised representation learning by sorting sequences"
    - "improved regularization of convolutional neural networks with cutout"
    - "deep unsupervised similarity learning using partially ordered sets"

Connected through hyperedge HE-6 (induced by source paper "learning user preferences and understanding calendar contexts for event scheduling"):
  with members:
    - "calendar help designing a workflow based scheduling agent with humans in the loop"
    - "bidirectional attention flow for machine comprehension"
    - "neural architectures for named entity recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "character aware neural language models"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "recurrent neural network regularization"

Connected through hyperedge HE-7 (induced by source paper "recurrent ladder networks"):
  with members:
    - "semi supervised phoneme recognition with recurrent ladder networks"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "cortexnet a generic network family for robust visual temporal representations"
    - "neural expectation maximization"
    - "on the exact relationship between the denoising function and the data distribution"
    - "unsupervised learning for physical interaction through video prediction"
    - "stacked hourglass networks for human pose estimation"
    - "learning recurrent representations for hierarchical behavior modeling"

Connected through hyperedge HE-8 (induced by source paper "neural expectation maximization"):
  with members:
    - "recurrent ladder networks"
    - "sfm net learning of structure and motion from video"
    - "learning features by watching objects move"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "tagger deep unsupervised perceptual grouping"
    - "learning visual groups from co occurrences in space and time"
    - "adam a method for stochastic optimization"
    - "deep unfolding model based inspiration of novel deep architectures"

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.LG(Machine Learning)