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: "word2vec parameter learning explained"
  Abstract: The word2vec model and application by Mikolov et al. have attracted a great amount of attention in recent two years. The vector representations of words learned by word2vec models have been shown to carry semantic meanings and are useful in various NLP tasks. As an increasing number of researchers would like to experiment with word2vec or similar techniques, I notice that there lacks a material that comprehensively explains the parameter learning process of word embedding models in details, thus preventing researchers that are non-experts in neural networks from understanding the working mechanism of such models. #R##N#This note provides detailed derivations and explanations of the parameter update equations of the word2vec models, including the original continuous bag-of-word (CBOW) and skip-gram (SG) models, as well as advanced optimization techniques, including hierarchical softmax and negative sampling. Intuitive interpretations of the gradient equations are also provided alongside mathematical derivations. #R##N#In the appendix, a review on the basics of neuron networks and backpropagation is provided. I also created an interactive demo, wevi, to facilitate the intuitive understanding of the model.

Connected through hyperedge HE-1 (induced by source paper "secnlp a survey of embeddings in clinical natural language processing"):
  with members:
    - "classifying medical relations in clinical text via convolutional neural networks"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "explainable prediction of medical codes from clinical text"
    - "embedding transfer for low resource medical named entity recognition a case study on patient mobility"
    - "medical concept embedding with time aware attention"
    - "patient representation learning and interpretable evaluation using clinical notes"
    - "semantic structure and interpretability of word embeddings"
    - "improving medical short text classification with semantic expansion using word cluster embedding"

Connected through hyperedge HE-2 (induced by source paper "distributional semantic modeling a revised technique to train term word vector space models applying the ontology related approach"):
  with members:
    - "development of formal models algorithms procedures engineering and functioning of the software system instrumental complex for ontological engineering purpose"
    - "deep contextualized word representations"
    - "integrated tools for engineering ontologies"
    - "to the problem of the instrumental complex for ontological engineering purpose software system design"
    - "research and development workstation environment the new class of current research information systems"
    - "word2vec applied to recommendation hyperparameters matter"
    - "technique for designing a domain ontology"
    - "fasttext zip compressing text classification models"

Connected through hyperedge HE-3 (induced by source paper "tedl a text encryption method based on deep learning"):
  with members:
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "local explanation methods for deep neural networks lack sensitivity to parameter values"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "lightweight efficient multi keyword ranked search over encrypted cloud data using dual word embeddings"
    - "recent trends in deep learning based natural language processing"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "membership inference attacks against machine learning models"
    - "towards biologically plausible deep learning"

Connected through hyperedge HE-4 (induced by source paper "eventmapper detecting real world physical events using corroborative and probabilistic sources"):
  with members:
    - "concept drift detection and adaptation with weak supervision on streaming unlabeled data"
    - "assed a framework for identifying physical events through adaptive social sensor data filtering"
    - "concept drift adaptive physical event detection for social media streams"
    - "convolutional neural networks for sentence classification"
    - "diffusion of lexical change in social media"

Connected through hyperedge HE-5 (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"

Connected through hyperedge HE-6 (induced by source paper "text classification algorithms a survey"):
  with members:
    - "rmdl random multimodel deep learning for classification"
    - "analysis of railway accidents narratives using deep learning"
    - "one shot and few shot learning of word embeddings"
    - "deep contextualized word representations"
    - "an improvement of data classification using random multimodel deep learning rmdl"
    - "gakco a fast gapped k mer string kernel using counting"
    - "learning important features through propagating activation differences"
    - "opening the black box of deep neural networks via information"

Connected through hyperedge HE-7 (induced by source paper "deep transfer learning for source code modeling"):
  with members:
    - "code2vec learning distributed representations of code"
    - "an exploration of dropout with rnns for natural language inference"
    - "autofolding for source code summarization"
    - "attention is all you need"
    - "decision support from financial disclosures with deep neural networks and transfer learning"
    - "collective intelligence for smarter api recommendations in python"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "deep api learning"

Connected through hyperedge HE-8 (induced by source paper "sorting and transforming program repair ingredients via deep learning code similarities"):
  with members:
    - "on learning meaningful code changes via neural machine translation"
    - "automatic software repair a bibliography"
    - "automatic repair of real bugs in java a large scale experiment on the defects4j dataset"
    - "nopol automatic repair of conditional statement bugs in java programs"
    - "automated correction for syntax errors in programming assignments using recurrent neural networks"
    - "sk_p a neural program corrector for moocs"
    - "mining software repair models for reasoning on the search space of automated program fixing"
    - "learning api usages from bytecode a statistical approach"

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)