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: "von mises fisher loss for training sequence to sequence models with continuous outputs"
  Abstract: The Softmax function is used in the final layer of nearly all existing sequence-to-sequence models for language generation. However, it is usually the slowest layer to compute which limits the vocabulary size to a subset of most frequent types; and it has a large memory footprint. We propose a general technique for replacing the softmax layer with a continuous embedding layer. Our primary innovations are a novel probabilistic loss, and a training and inference procedure in which we generate a probability distribution over pre-trained word embeddings, instead of a multinomial distribution over the vocabulary obtained via softmax. We evaluate this new class of sequence-to-sequence models with continuous outputs on the task of neural machine translation. We show that our models obtain upto 2.5x speed-up in training time while performing on par with the state-of-the-art models in terms of translation quality. These models are capable of handling very large vocabularies without compromising on translation quality. They also produce more meaningful errors than in the softmax-based models, as these errors typically lie in a subspace of the vector space of the reference translations.

Connected through hyperedge HE-1 (induced by source paper "directional deep embedding and appearance learning for fast video object segmentation"):
  with members:
    - "deeper and wider siamese networks for real time visual tracking"
    - "rvos end to end recurrent network for video object segmentation"
    - "feelvos fast end to end embedding learning for video object segmentation"
    - "atom accurate tracking by overlap maximization"
    - "fast and accurate online video object segmentation via tracking parts"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "efficient video object segmentation via network modulation"
    - "directional statistics based deep metric learning for image classification and retrieval"

Connected through hyperedge HE-2 (induced by source paper "efficient contextual representation learning without softmax layer"):
  with members:
    - "multilingual constituency parsing with self attention and pre training"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "higher order coreference resolution with coarse to fine inference"
    - "an analysis of neural language modeling at multiple scales"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dissecting contextual word embeddings architecture and representation"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"

Connected through hyperedge HE-3 (induced by source paper "regressing word and sentence embeddings for regularization of neural machine translation"):
  with members:
    - "rewe regressing word embeddings for regularization of neural machine translation systems"
    - "pre trained language model representations for language generation"
    - "differentiable sampling with flexible reference word order for neural machine translation"
    - "regularizing neural machine translation by target bidirectional agreement"
    - "training neural machine translation using word embedding based loss"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "deep contextualized word representations"
    - "universal sentence encoder"

Connected through hyperedge HE-4 (induced by source paper "spherical text embedding"):
  with members:
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "poincar e glove hyperbolic word embeddings"
    - "embedding text in hyperbolic spaces"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "weakly supervised hierarchical text classification"
    - "weakly supervised neural text classification"
    - "hyperbolic entailment cones for learning hierarchical embeddings"
    - "deep hyperspherical learning"

Connected through hyperedge HE-5 (induced by source paper "an embarrassingly simple approach for transfer learning from pretrained language models"):
  with members:
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "deep contextualized word representations for detecting sarcasm and irony"
    - "ntua slp at iest 2018 ensemble of neural transfer methods for implicit emotion classification"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "spell once summon anywhere a two level open vocabulary language model"
    - "universal language model fine tuning for text classification"
    - "bb_twtr at semeval 2017 task 4 twitter sentiment analysis with cnns and lstms"

Connected through hyperedge HE-6 (induced by source paper "sparse sequence to sequence models"):
  with members:
    - "attention is not explanation"
    - "sparse and constrained attention for neural machine translation"
    - "hard non monotonic attention for character level transduction"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "classical structured prediction losses for sequence to sequence learning"
    - "massively multilingual neural grapheme to phoneme conversion"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"

Connected through hyperedge HE-7 (induced by source paper "discriminative few shot learning based on directional statistics"):
  with members:
    - "meta learning with latent embedding optimization"
    - "cosface large margin cosine loss for deep face recognition"
    - "learning embedding adaptation for few shot learning"
    - "directional statistics based deep metric learning for image classification and retrieval"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "dynamic few shot visual learning without forgetting"
    - "low shot learning with imprinted weights"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-8 (induced by source paper "generative adversarial networks for text using word2vec intermediaries"):
  with members:
    - "neural text generation past present and beyond"
    - "towards understanding linear word analogies"
    - "large scale gan training for high fidelity natural image synthesis"
    - "texygen a benchmarking platform for text generation models"
    - "adversarial generation of natural language"
    - "adversarial ranking for language generation"
    - "improved training of wasserstein gans"
    - "maximum likelihood augmented discrete generative adversarial networks"

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.CL(Computation and Language)