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: "towards understanding learning representations to what extent do different neural networks learn the same representation"
  Abstract: It is widely believed that learning good representations is one of the main reasons for the success of deep neural networks. Although highly intuitive, there is a lack of theory and systematic approach quantitatively characterizing what representations do deep neural networks learn. In this work, we move a tiny step towards a theory and better understanding of the representations. Specifically, we study a simpler problem: How similar are the representations learned by two networks with identical architecture but trained from different initializations. We develop a rigorous theory based on the neuron activation subspace match model. The theory gives a complete characterization of the structure of neuron activation subspace matches, where the core concepts are maximum match and simple match which describe the overall and the finest similarity between sets of neurons in two networks respectively. We also propose efficient algorithms to find the maximum match and simple matches. Finally, we conduct extensive experiments using our algorithms. Experimental results suggest that, surprisingly, representations learned by the same convolutional layers of networks trained from different initializations are not as similar as prevalently expected, at least in terms of subspace match.

Connected through hyperedge HE-1 (induced by source paper "emerging cross lingual structure in pretrained language models"):
  with members:
    - "similarity of neural network representations revisited"
    - "cross lingual bert transformation for zero shot dependency parsing"
    - "unicoder a universal language encoder by pre training with multiple cross lingual tasks"
    - "polyglot contextual representations improve crosslingual transfer"
    - "analyzing the limitations of cross lingual word embedding mappings"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "bilingual lexicon induction with semi supervision in non isometric embedding spaces"
    - "investigating multilingual nmt representations at scale"

Connected through hyperedge HE-2 (induced by source paper "knowledge consistency between neural networks and beyond"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "similarity of neural network representations revisited"
    - "quantifying layerwise information discarding of neural networks"
    - "towards a unified evaluation of explanation methods without ground truth"
    - "utility analysis of network architectures for 3d point cloud processing"
    - "stiffness a new perspective on generalization in neural networks"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "network dissection quantifying interpretability of deep visual representations"

Connected through hyperedge HE-3 (induced by source paper "characterizing the shape of activation space in deep neural networks"):
  with members:
    - "topology of learning in artificial neural networks"
    - "neural persistence a complexity measure for deep neural networks using algebraic topology"
    - "adversarial examples a complete characterisation of the phenomenon"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "on characterizing the capacity of neural networks using algebraic topology"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "detecting adversarial perturbations through spatial behavior in activation spaces"

Connected through hyperedge HE-4 (induced by source paper "similarity of neural network representations revisited"):
  with members:
    - "tensor2tensor for neural machine translation"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "skip connections eliminate singularities"
    - "attention is all you need"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "perceptual losses for real time style transfer and super resolution"
    - "density estimation using real nvp"
    - "a learned representation for artistic style"

Connected through hyperedge HE-5 (induced by source paper "embedding comparator visualizing differences in global structure and local neighborhoods via small multiples"):
  with members:
    - "learning protein sequence embeddings using information from structure"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "what made you do this understanding black box decisions with sufficient input subsets"
    - "universal language model fine tuning for text classification"
    - "network dissection quantifying interpretability of deep visual representations"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "axiomatic attribution for deep networks"
    - "advances in pre training distributed word representations"

Connected through hyperedge HE-6 (induced by source paper "confidence calibration for convolutional neural networks using structured dropout"):
  with members:
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "dropblock a regularization method for convolutional networks"
    - "bias reduced uncertainty estimation for deep neural classifiers"
    - "averaging weights leads to wider optima and better generalization"
    - "the description length of deep learning models"
    - "on calibration of modern neural networks"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "deep bayesian active learning with image data"

Connected through hyperedge HE-7 (induced by source paper "all neural networks are created equal"):
  with members:
    - "on the power of curriculum learning in training deep networks"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "a style based generator architecture for generative adversarial networks"
    - "a convergence analysis of gradient descent for deep linear neural networks"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "understanding deep learning requires rethinking generalization"
    - "densely connected convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "blackbox meets blackbox representational similarity and stability analysis of neural language models and brains"):
  with members:
    - "robust evaluation of language brain encoding experiments"
    - "correlating neural and symbolic representations of language"
    - "under the hood using diagnostic classifiers to investigate and improve how language models track agreement information"
    - "gromov wasserstein alignment of word embedding spaces"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "sharp nearby fuzzy far away how neural language models use context"
    - "experiential distributional and dependency based word embeddings have complementary roles in decoding brain activity"

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)