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: "contextual affective analysis a case study of people portrayals in online metoo stories"
  Abstract: In October 2017, numerous women accused producer Harvey Weinstein of sexual harassment. Their stories encouraged other women to voice allegations of sexual harassment against many high profile men, including politicians, actors, and producers. These events are broadly referred to as the #MeToo movement, named for the use of the hashtag "#metoo" on social media platforms like Twitter and Facebook. The movement has widely been referred to as "empowering" because it has amplified the voices of previously unheard women over those of traditionally powerful men. In this work, we investigate dynamics of sentiment, power and agency in online media coverage of these events. Using a corpus of online media articles about the #MeToo movement, we present a contextual affective analysis---an entity-centric approach that uses contextualized lexicons to examine how people are portrayed in media articles. We show that while these articles are sympathetic towards women who have experienced sexual harassment, they consistently present men as most powerful, even after sexual assault allegations. While we focus on media coverage of the #MeToo movement, our method for contextual affective analysis readily generalizes to other domains.

Connected through hyperedge HE-1 (induced by source paper "a framework for the computational linguistic analysis of dehumanization"):
  with members:
    - "social bias frames reasoning about social and power implications of language"
    - "mitigating gender bias in natural language processing literature review"
    - "talkdown a corpus for condescension detection in context"
    - "automatically neutralizing subjective bias in text"
    - "black is to criminal as caucasian is to police detecting and removing multiclass bias in word embeddings"
    - "entity centric contextual affective analysis"
    - "examining gender and race bias in two hundred sentiment analysis systems"
    - "gender bias in coreference resolution"

Connected through hyperedge HE-2 (induced by source paper "metoo on campus studying college sexual assault at scale using data reported on social media"):
  with members:
    - "multimodal social media analysis for gang violence prevention"
    - "hate lingo a target based linguistic analysis of hate speech in social media"
    - "detecting the hate code on social media"
    - "using social media to promote stem education matching college students with role models"

Connected through hyperedge HE-3 (induced by source paper "social bias frames reasoning about social and power implications of language"):
  with members:
    - "talkdown a corpus for condescension detection in context"
    - "unifying human and statistical evaluation for natural language generation"
    - "model cards for model reporting"
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "cooperative generator discriminator networks for abstractive summarization with narrative flow"
    - "predicting the type and target of offensive posts in social media"
    - "racial bias in hate speech and abusive language detection datasets"
    - "winogrande an adversarial winograd schema challenge at scale"

Connected through hyperedge HE-4 (induced by source paper "entity centric contextual affective analysis"):
  with members:
    - "assessing bert s syntactic abilities"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "measuring bias in contextualized word representations"
    - "gender bias in contextualized word embeddings"
    - "publicly available clinical bert embeddings"
    - "linguistic knowledge and transferability of contextual representations"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "metooma multi aspect annotations of tweets related to the metoo movement"):
  with members:
    - "distributional semantics approach to detect intent in twitter conversations on sexual assaults"
    - "deep learning for hate speech detection in tweets"
    - "stance detection with bidirectional conditional encoding"
    - "crowdsourcing a word emotion association lexicon"

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.SI(Social and Information Networks)