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: "the emotional arcs of stories are dominated by six basic shapes"
  Abstract: Advances in computing power, natural language processing, and digitization of text now make it possible to study a culture’s evolution through its texts using a ‘big data’ lens. Our ability to communicate relies in part upon a shared emotional experience, with stories often following distinct emotional trajectories and forming patterns that are meaningful to us. Here, by classifying the emotional arcs for a filtered subset of 1,327 stories from Project Gutenberg’s fiction collection, we find a set of six core emotional arcs which form the essential building blocks of complex emotional trajectories. We strengthen our findings by separately applying matrix decomposition, supervised learning, and unsupervised learning. For each of these six core emotional arcs, we examine the closest characteristic stories in publication today and find that particular emotional arcs enjoy greater success, as measured by downloads.

Connected through hyperedge HE-1 (induced by source paper "shaping the narrative arc an information theoretic approach to collaborative dialogue"):
  with members:
    - "predicting movie genres based on plot summaries"
    - "retrieve and refine improved sequence generation models for dialogue"
    - "generating wikipedia by summarizing long sequences"
    - "smart conversational agents for reminiscence"
    - "universal sentence encoder"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "toward controlled generation of text"
    - "generating sentences by editing prototypes"

Connected through hyperedge HE-2 (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"
    - "frowning frodo wincing leia and a seriously great friendship learning to classify emotional relationships of fictional characters"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "dens a dataset for multi class emotion analysis"):
  with members:
    - "depechemood a bilingual emotion lexicon built through simple yet powerful techniques"
    - "practical text classification with large pre trained language models"
    - "deep contextualized word representations"
    - "iest wassa 2018 implicit emotions shared task"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a structured self attentive sentence embedding"
    - "inferring networks of substitutable and complementary products"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-4 (induced by source paper "bhaav a text corpus for emotion analysis from hindi stories"):
  with members:
    - "deep learning for sentiment analysis a survey"
    - "stronger baselines for trustable results in neural machine translation"
    - "enriching word vectors with subword information"
    - "adam a method for stochastic optimization"
    - "scikit learn machine learning in python"

Connected through hyperedge HE-5 (induced by source paper "how the avengers assemble ecological modelling of effective cast sizes for movies"):
  with members:
    - "representation of texts as complex networks a mesoscopic approach"
    - "the data science of hollywood using emotional arcs of movies to drive business model innovation in entertainment industries"
    - "the one comparing narrative social network extraction techniques"
    - "early predictions of movie success the who what and when of profitability"
    - "a survey of available corpora for building data driven dialogue systems"

Connected through hyperedge HE-6 (induced by source paper "an analysis of emotion communication channels in fan fiction towards emotional storytelling"):
  with members:
    - "frowning frodo wincing leia and a seriously great friendship learning to classify emotional relationships of fictional characters"
    - "controllable neural story plot generation via reinforcement learning"
    - "mojitalk generating emotional responses at scale"
    - "wassa 2017 shared task on emotion intensity"

Connected through hyperedge HE-7 (induced by source paper "frowning frodo wincing leia and a seriously great friendship learning to classify emotional relationships of fictional characters"):
  with members:
    - "annotating character relationships in literary texts"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "crowdsourcing a word emotion association lexicon"

Connected through hyperedge HE-8 (induced by source paper "examining uk drill music through sentiment trajectory analysis"):
  with members:
    - "identifying the sentiment styles of youtube s vloggers"
    - "word embeddings to enhance twitter gang member profile identification"

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)