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: "binary paragraph vectors"
  Abstract: Recently Le & Mikolov described two log-linear models, called Paragraph Vector, that can be used to learn state-of-the-art distributed representations of documents. Inspired by this work, we present Binary Paragraph Vectors: simple neural networks that learn short binary codes for fast information retrieval. We show that binary paragraph vectors outperform autoencoder-based binary codes, despite using fewer bits. We also evaluate their precision in transfer learning settings, where binary codes are inferred for documents unrelated to the training corpus. Results from these experiments indicate that Binary Paragraph Vectors can capture semantics relevant for various domain-specific documents. Finally, we present a model that simultaneously learns short binary codes and longer, real-valued representations. This model can be used to rapidly retrieve a short list of highly relevant documents from a large document collection.

Connected through hyperedge HE-1 (induced by source paper "embedding compression with isotropic iterative quantization"):
  with members:
    - "senteval an evaluation toolkit for universal sentence representations"
    - "all but the top simple and effective postprocessing for word representations"
    - "compressing word embeddings via deep compositional code learning"
    - "bag of tricks for efficient text classification"
    - "neural architectures for named entity recognition"
    - "compression of neural machine translation models via pruning"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "a latent variable model approach to pmi based word embeddings"

Connected through hyperedge HE-2 (induced by source paper "vector representations of text data in deep learning"):
  with members:
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "starspace embed all the things"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "swivel improving embeddings by noticing what s missing"
    - "node2vec scalable feature learning for networks"
    - "problems with evaluation of word embeddings using word similarity tasks"
    - "a joint many task model growing a neural network for multiple nlp tasks"
    - "the concrete distribution a continuous relaxation of discrete random variables"

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)