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: "unsupervised learning on neural network outputs"
  Abstract: The outputs of a trained neural network contain much richer information than just an one-hot classifier. For example, a neural network might give an image of a dog the probability of one in a million of being a cat but it is still much larger than the probability of being a car. To reveal the hidden structure in them, we apply two unsupervised learning algorithms, PCA and ICA, to the outputs of a deep Convolutional Neural Network trained on the ImageNet of 1000 classes. The PCA/ICA embedding of the object classes reveals their visual similarity and the PCA/ICA components can be interpreted as common visual features shared by similar object classes. For an application, we proposed a new zero-shot learning method, in which the visual features learned by PCA/ICA are employed. Our zero-shot learning method achieves the state-of-the-art results on the ImageNet of over 20000 classes.

Connected through hyperedge HE-1 (induced by source paper "classifier and exemplar synthesis for zero shot learning"):
  with members:
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "rethinking knowledge graph propagation for zero shot learning"
    - "semantic autoencoder for zero shot learning"
    - "a simple exponential family framework for zero shot learning"
    - "the devil is in the tails fine grained classification in the wild"
    - "from zero shot learning to conventional supervised classification unseen visual data synthesis"
    - "zero shot learning a comprehensive evaluation of the good the bad and the ugly"
    - "a generative adversarial approach for zero shot learning from noisy texts"

Connected through hyperedge HE-2 (induced by source paper "visual interpretability for deep learning a survey"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "interpreting cnns via decision trees"
    - "network dissection quantifying interpretability of deep visual representations"
    - "towards interpretable r cnn by unfolding latent structures"
    - "interactively transferring cnn patterns for part localization"
    - "explaining the unexplained a class enhanced attentive response clear approach to understanding deep neural networks"
    - "interpreting cnn knowledge via an explanatory graph"
    - "dynamic routing between capsules"

Connected through hyperedge HE-3 (induced by source paper "predicting visual exemplars of unseen classes for zero shot learning"):
  with members:
    - "from zero shot learning to conventional supervised classification unseen visual data synthesis"
    - "icarl incremental classifier and representation learning"
    - "learning a deep embedding model for zero shot learning"
    - "gaze embeddings for zero shot image classification"
    - "generative adversarial text to image synthesis"
    - "synthesized classifiers for zero shot learning"
    - "zero shot visual recognition via bidirectional latent embedding"
    - "an empirical study and analysis of generalized zero shot learning for object recognition in the wild"

Connected through hyperedge HE-4 (induced by source paper "growing interpretable part graphs on convnets via multi shot learning"):
  with members:
    - "inverting visual representations with convolutional networks"
    - "what happened to my dog in that network unraveling top down generators in convolutional neural networks"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "deep residual learning for image recognition"
    - "deep learning for semantic part segmentation with high level guidance"
    - "understanding deep features with computer generated imagery"
    - "fast r cnn"
    - "unsupervised learning on neural network outputs with application in zero shot learning"

Connected through hyperedge HE-5 (induced by source paper "synthesized classifiers for zero shot learning"):
  with members:
    - "how to transfer zero shot object recognition via hierarchical transfer of semantic attributes"
    - "transductive multi view zero shot learning"
    - "active transfer learning with zero shot priors reusing past datasets for future tasks"
    - "evaluation of output embeddings for fine grained image classification"
    - "zero shot learning via semantic similarity embedding"
    - "unsupervised learning on neural network outputs with application in zero shot learning"
    - "predicting deep zero shot convolutional neural networks using textual descriptions"
    - "zero shot learning via joint latent similarity embedding"

Connected through hyperedge HE-6 (induced by source paper "transfer learning for speech and language processing"):
  with members:
    - "recognize foreign low frequency words with similar pairs"
    - "knowledge transfer pre training"
    - "learning transferable features with deep adaptation networks"
    - "learning from lda using deep neural networks"
    - "distilling word embeddings an encoding approach"
    - "unsupervised learning on neural network outputs with application in zero shot learning"
    - "transferring knowledge from a rnn to a dnn"
    - "fitnets hints for thin deep nets"

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)