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: "frowning frodo wincing leia and a seriously great friendship learning to classify emotional relationships of fictional characters"
  Abstract: The development of a fictional plot is centered around characters who closely interact with each other forming dynamic social networks. In literature analysis, such networks have mostly been analyzed without particular relation types or focusing on roles which the characters take with respect to each other. We argue that an important aspect for the analysis of stories and their development is the emotion between characters. In this paper, we combine these aspects into a unified framework to classify emotional relationships of fictional characters. We formalize it as a new task and describe the annotation of a corpus, based on fan-fiction short stories. The extraction pipeline which we propose consists of character identification (which we treat as given by an oracle here) and the relation classification. For the latter, we provide results using several approaches previously proposed for relation identification with neural methods. The best result of 0.45 F1 is achieved with a GRU with character position indicators on the task of predicting undirected emotion relations in the associated social network graph.

Connected through hyperedge HE-1 (induced by source paper "goodnewseveryone a corpus of news headlines annotated with emotions semantic roles and reader perception"):
  with members:
    - "po emo conceptualization annotation and modeling of aesthetic emotions in german and english poetry"
    - "emotion cause pair extraction a new task to emotion analysis in texts"
    - "utilizing bert for aspect based sentiment analysis via constructing auxiliary sentence"
    - "rthn a rnn transformer hierarchical network for emotion cause extraction"
    - "from independent prediction to re ordered prediction integrating relative position and global label information to emotion cause identification"
    - "crowdsourcing and validating event focused emotion corpora for german and english"
    - "modeling word emotion in historical language quantity beats supposed stability in seed word selection"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (induced by source paper "an analysis of emotion communication channels in fan fiction towards emotional storytelling"):
  with members:
    - "controllable neural story plot generation via reinforcement learning"
    - "mojitalk generating emotional responses at scale"
    - "wassa 2017 shared task on emotion intensity"
    - "the emotional arcs of stories are dominated by six basic shapes"

Connected through hyperedge HE-3 (induced by source paper "a survey on sentiment and emotion analysis for computational literary studies"):
  with members:
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "an analysis of emotion communication channels in fan fiction towards emotional storytelling"
    - "deep learning for sentiment analysis a survey"
    - "the evolution of sentiment analysis a review of research topics venues and top cited papers"
    - "assessing state of the art sentiment models on state of the art sentiment datasets"
    - "wassa 2017 shared task on emotion intensity"
    - "the emotional arcs of stories are dominated by six basic shapes"
    - "distributed representations of words and phrases and their compositionality"

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)