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: "a la carte embedding cheap but effective induction of semantic feature vectors"
  Abstract: Motivations like domain adaptation, transfer learning, and feature learning have fueled interest in inducing embeddings for rare or unseen words, n-grams, synsets, and other textual features. This paper introduces a la carte embedding, a simple and general alternative to the usual word2vec-based approaches for building such representations that is based upon recent theoretical results for GloVe-like embeddings. Our method relies mainly on a linear transformation that is efficiently learnable using pretrained word vectors and linear regression. This transform is applicable on the fly in the future when a new text feature or rare word is encountered, even if only a single usage example is available. We introduce a new dataset showing how the a la carte method requires fewer examples of words in context to learn high-quality embeddings and we obtain state-of-the-art results on a nonce task and some unsupervised document classification tasks.

Connected through hyperedge HE-1 (induced by source paper "improving neural language modeling via adversarial training"):
  with members:
    - "partially shuffling the training data to improve language models"
    - "representation degeneration problem in training natural language generation models"
    - "fast parametric learning with activation memorization"
    - "learning towards minimum hyperspherical energy"
    - "frage frequency agnostic word representation"
    - "an analysis of neural language modeling at multiple scales"
    - "darts differentiable architecture search"
    - "additive margin softmax for face verification"

Connected through hyperedge HE-2 (induced by source paper "learning task specific representation for novel words in sequence labeling"):
  with members:
    - "chinese ner using lattice lstm"
    - "learning semantic representations for novel words leveraging both form and context"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "mimicking word embeddings using subword rnns"
    - "end to end learning for self driving cars"
    - "towards cross lingual distributed representations without parallel text trained with adversarial autoencoders"
    - "finding function in form compositional character models for open vocabulary word representation"

Connected through hyperedge HE-3 (induced by source paper "restricted isometry property under high correlations"):
  with members:
    - "sketching as a tool for numerical linear algebra"
    - "exact recovery of sparsely used dictionaries"
    - "new and improved johnson lindenstrauss embeddings via the restricted isometry property"

Connected through hyperedge HE-4 (induced by source paper "parameter free sentence embedding via orthogonal basis"):
  with members:
    - "learning semantic textual similarity from conversations"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "an efficient framework for learning sentence representations"
    - "concatenated p mean word embeddings as universal cross lingual sentence representations"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "supervised learning of universal sentence representations from natural language inference data"

Connected through hyperedge HE-5 (induced by source paper "unsupervised post processing of word vectors via conceptor negation"):
  with members:
    - "fully statistical neural belief tracking"
    - "semantic specialisation of distributional word vector spaces using monolingual and cross lingual constraints"
    - "all but the top simple and effective postprocessing for word representations"
    - "a network based end to end trainable task oriented dialogue system"
    - "enriching word vectors with subword information"
    - "simverb 3500 a large scale evaluation set of verb similarity"
    - "towards universal paraphrastic sentence embeddings"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"

Connected through hyperedge HE-6 (induced by source paper "correcting the common discourse bias in linear representation of sentences using conceptors"):
  with members:
    - "universal sentence encoder"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"
    - "all but the top simple and effective postprocessing for word representations"
    - "representing sentences as low rank subspaces"
    - "towards universal paraphrastic sentence embeddings"
    - "retrofitting word vectors to semantic lexicons"
    - "a convolutional neural network for modelling sentences"

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)