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: "recurrent ladder networks"
  Abstract: We propose a recurrent extension of the Ladder networks whose structure is motivated by the inference required in hierarchical latent variable models. We demonstrate that the recurrent Ladder is able to handle a wide variety of complex learning tasks that benefit from iterative inference and temporal modeling. The architecture shows close-to-optimal results on temporal modeling of video data, competitive results on music modeling, and improved perceptual grouping based on higher order abstractions, such as stochastic textures and motion cues. We present results for fully supervised, semi-supervised, and unsupervised tasks. The results suggest that the proposed architecture and principles are powerful tools for learning a hierarchy of abstractions, learning iterative inference and handling temporal information.

Connected through hyperedge HE-1 (induced by source paper "learning temporal action proposals with fewer labels"):
  with members:
    - "d3tw discriminative differentiable dynamic time warping for weakly supervised action alignment and segmentation"
    - "ctap complementary temporal action proposal generation"
    - "semi supervised sequence modeling with cross view training"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "bsn boundary sensitive network for temporal action proposal generation"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "single shot temporal action detection"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"

Connected through hyperedge HE-2 (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-3 (induced by source paper "implicit dimension identification in user generated text with lstm networks"):
  with members:
    - "deep learning for hate speech detection in tweets"
    - "fake news detection on social media a data mining perspective"
    - "w2vlda almost unsupervised system for aspect based sentiment analysis"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "pointer sentinel mixture models"
    - "morphological inflection generation with hard monotonic attention"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "tensorflow a system for large scale machine learning"

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"
    - "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"
    - "binding via reconstruction clustering"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "sequential attend infer repeat generative modelling of moving objects"):
  with members:
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "world models"
    - "stochastic video generation with a learned prior"
    - "learning to decompose and disentangle representations for video prediction"
    - "end to end representation learning for correlation filter based tracking"
    - "unsupervised learning of disentangled representations from video"
    - "a simple neural network module for relational reasoning"
    - "neural expectation maximization"

Connected through hyperedge HE-6 (induced by source paper "relational neural expectation maximization unsupervised discovery of objects and their interactions"):
  with members:
    - "deep sets"
    - "inferring and executing programs for visual reasoning"
    - "visual interaction networks"
    - "a simple neural network module for relational reasoning"
    - "making neural programming architectures generalize via recursion"
    - "learning to reason end to end module networks for visual question answering"
    - "schema networks zero shot transfer with a generative causal model of intuitive physics"
    - "neural message passing for quantum chemistry"

Connected through hyperedge HE-7 (induced by source paper "location dependency in video prediction"):
  with members:
    - "conditional image generation with pixelcnn decoders"
    - "video ladder networks"
    - "location sensitive deep convolutional neural networks for segmentation of white matter hyperintensities"
    - "video pixel networks"
    - "deepfix a fully convolutional neural network for predicting human eye fixations"
    - "semi supervised learning with ladder networks"
    - "deep multi scale video prediction beyond mean square error"

Connected through hyperedge HE-8 (induced by source paper "ladder networks for semi supervised hyperspectral image classification"):
  with members:
    - "semi supervised learning with ladder networks"
    - "adam a method for stochastic optimization"

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.NE(Neural and Evolutionary Computing)