human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "constructing a highlight classifier with an attention based lstm neural network"):
  Abstract: Data is being produced in larger quantities than ever before in human history. It's only natural to expect a rise in demand for technology that aids humans in sifting through and analyzing this inexhaustible supply of information. This need exists in the market research industry, where large amounts of consumer research data is collected through video recordings. At present, the standard method for analyzing video data is human labor. Market researchers manually review the vast majority of consumer research video in order to identify relevant portions - highlights. The industry state of the art turnaround ratio is 2.2 - for every hour of video content 2.2 hours of manpower are required. In this study we present a novel approach for NLP-based highlight identification and extraction based on a supervised learning model that aides market researchers in sifting through their data. Our approach hinges on a manually curated user-generated highlight clips constructed from long and short-form video data. The problem is best suited for an NLP approach due to the availability of video transcription. We evaluate multiple classes of models, from gradient boosting to recurrent neural networks, comparing their performance in extraction and identification of highlights. The best performing models are then evaluated using four sampling methods designed to analyze documents much larger than the maximum input length of the classifiers. We report very high performances for the standalone classifiers, ROC AUC scores in the range 0.93-0.94, but observe a significant drop in effectiveness when evaluated on large documents. Based on our results we suggest combinations of models/sampling algorithms for various use cases.

Includes members:
  Member "personalized neural embeddings for collaborative filtering with text":
    also connected through other hyperedges:
      (none)
  Member "deep bidirectional and unidirectional lstm recurrent neural network for network wide traffic speed prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "interpretable crowd flow prediction with spatial temporal self attention")
  Member "deep learning for sentiment analysis a survey":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an end to end visual audio attention network for emotion recognition in user generated videos")
  Member "generalizing word embeddings using bag of subwords":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hierarchical character embeddings learning phonological and semantic representations in languages of logographic origin using recursive neural networks")
  Member "word mover s embedding from word2vec to document embedding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "structural aware sentence similarity with recursive optimal transport")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "automatic music highlight extraction using convolutional recurrent attention networks":
    also connected through other hyperedges:
      (none)
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

Question: Please tell me which class the source paper 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)