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: "analyzing polarization in social media method and application to tweets on 21 mass shootings"
  Abstract: We provide an NLP framework to uncover four linguistic dimensions of political polarization in social media: topic choice, framing, affect and illocutionary force. We quantify these aspects with existing lexical methods, and propose clustering of tweet embeddings as a means to identify salient topics for analysis across events; human evaluations show that our approach generates more cohesive topics than traditional LDA-based models. We apply our methods to study 4.4M tweets on 21 mass shootings. We provide evidence that the discussion of these events is highly polarized politically and that this polarization is primarily driven by partisan differences in framing rather than topic choice. We identify framing devices, such as grounding and the contrasting use of the terms "terrorist" and "crazy", that contribute to polarization. Results pertaining to topic choice, affect and illocutionary force suggest that Republicans focus more on the shooter and event-specific facts (news) while Democrats focus more on the victims and call for policy changes. Our work contributes to a deeper understanding of the way group divisions manifest in language and to computational methods for studying them.

Connected through hyperedge HE-1 (induced by source paper "social media attributions in the context of water crisis"):
  with members:
    - "who blames whom in a crisis detecting blame ties from news articles using neural networks"
    - "voice for the voiceless active sampling to detect comments supporting the rohingyas"
    - "kashmir a computational analysis of the voice of peace"
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "huggingface s transformers state of the art natural language processing"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attribution of responsibility and blame regarding a man made disaster flintwatercrisis"

Connected through hyperedge HE-2 (induced by source paper "automatically inferring gender associations from language"):
  with members:
    - "gender bias in contextualized word embeddings"
    - "lipstick on a pig debiasing methods cover up systematic gender biases in word embeddings but do not remove them"
    - "word embeddings quantify 100 years of gender and ethnic stereotypes"
    - "learning gender neutral word embeddings"
    - "semantics derived automatically from language corpora contain human like biases"
    - "tie breaker using language models to quantify gender bias in sports journalism"
    - "women through the glass ceiling gender asymmetries in wikipedia"
    - "shirtless and dangerous quantifying linguistic signals of gender bias in an online fiction writing community"

Connected through hyperedge HE-3 (induced by source paper "knowbias a novel ai method to detect polarity in online content"):
  with members:
    - "knowbias detecting political polarity in long text content"
    - "universal sentence encoder"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "recommender system for news articles using supervised learning"
    - "rationalizing neural predictions"
    - "distributed representations of words and phrases and their compositionality"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-4 (induced by source paper "knowbias detecting political polarity in long text content"):
  with members:
    - "knowbias a novel ai method to detect polarity in online content"
    - "universal sentence encoder"
    - "rationalizing neural predictions"

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)