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: "talkdown a corpus for condescension detection in context"
  Abstract: Condescending language use is caustic; it can bring dialogues to an end and bifurcate communities. Thus, systems for condescension detection could have a large positive impact. A challenge here is that condescension is often impossible to detect from isolated utterances, as it depends on the discourse and social context. To address this, we present TalkDown, a new labeled dataset of condescending linguistic acts in context. We show that extending a language-only model with representations of the discourse improves performance, and we motivate techniques for dealing with the low rates of condescension overall. We also use our model to estimate condescension rates in various online communities and relate these differences to differing community norms.

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"
    - "automatically neutralizing subjective bias in text"
    - "black is to criminal as caucasian is to police detecting and removing multiclass bias in word embeddings"
    - "contextual affective analysis a case study of people portrayals in online metoo stories"
    - "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 "social bias frames reasoning about social and power implications of language"):
  with members:
    - "unifying human and statistical evaluation for natural language generation"
    - "model cards for model reporting"
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "contextual affective analysis a case study of people portrayals in online metoo stories"
    - "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"

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)