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 "a survey on sentiment and emotion analysis for computational literary studies"):
  Abstract: Emotions have often been a crucial part of compelling narratives: literature tells about people with goals, desires, passions, and intentions. In the past, classical literary studies usually scrutinized the affective dimension of literature within the framework of hermeneutics. However, with emergence of the research field known as Digital Humanities (DH) some studies of emotions in literary context have taken a computational turn. Given the fact that DH is still being formed as a science, this direction of research can be rendered relatively new. At the same time, the research in sentiment analysis started in computational linguistic almost two decades ago and is nowadays an established field that has dedicated workshops and tracks in the main computational linguistics conferences. This leads us to the question of what are the commonalities and discrepancies between sentiment analysis research in computational linguistics and digital humanities? In this survey, we offer an overview of the existing body of research on sentiment and emotion analysis as applied to literature. We precede the main part of the survey with a short introduction to natural language processing and machine learning, psychological models of emotions, and provide an overview of existing approaches to sentiment and emotion analysis in computational linguistics. The papers presented in this survey are either coming directly from DH or computational linguistics venues and are limited to sentiment and emotion analysis as applied to literary text.

Includes members:
  Member "frowning frodo wincing leia and a seriously great friendship learning to classify emotional relationships of fictional characters":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "goodnewseveryone a corpus of news headlines annotated with emotions semantic roles and reader perception")
  Member "semeval 2017 task 4 sentiment analysis in twitter":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "related tasks can share a multi task framework for affective language")
  Member "an analysis of emotion communication channels in fan fiction towards emotional storytelling":
    also connected through other hyperedges:
      (none)
  Member "deep learning for sentiment analysis a survey":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an end to end visual audio attention network for emotion recognition in user generated videos")
  Member "the evolution of sentiment analysis a review of research topics venues and top cited papers":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "natural language or not nlon a package for software engineering text analysis pipeline")
  Member "assessing state of the art sentiment models on state of the art sentiment datasets":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unsupervised transfer learning via bert neuron selection")
  Member "wassa 2017 shared task on emotion intensity":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "related tasks can share a multi task framework for affective language")
  Member "the emotional arcs of stories are dominated by six basic shapes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "shaping the narrative arc an information theoretic approach to collaborative dialogue")

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)