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: "connotation frames a data driven investigation"
  Abstract: Through a particular choice of a predicate (e.g., "x violated y"), a writer can subtly connote a range of implied sentiments and presupposed facts about the entities x and y: (1) writer's perspective: projecting x as an "antagonist"and y as a "victim", (2) entities' perspective: y probably dislikes x, (3) effect: something bad happened to y, (4) value: y is something valuable, and (5) mental state: y is distressed by the event. We introduce connotation frames as a representation formalism to organize these rich dimensions of connotation using typed relations. First, we investigate the feasibility of obtaining connotative labels through crowdsourcing experiments. We then present models for predicting the connotation frames of verb predicates based on their distributional word representations and the interplay between different types of connotative relations. Empirical results confirm that connotation frames can be induced from various data sources that reflect how people use language and give rise to the connotative meanings. We conclude with analytical results that show the potential use of connotation frames for analyzing subtle biases in online news media.

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"
    - "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"

Connected through hyperedge HE-2 (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"
    - "contextual affective analysis a case study of people portrayals in online metoo stories"
    - "gender bias in contextualized word embeddings"
    - "publicly available clinical bert embeddings"
    - "linguistic knowledge and transferability of contextual representations"
    - "deep contextualized word representations"

Connected through hyperedge HE-3 (induced by source paper "contextual affective analysis a case study of people portrayals in online metoo stories"):
  with members:
    - "deep contextualized word representations"
    - "dialog structure through the lens of gender gender environment and power"
    - "inducing domain specific sentiment lexicons from unlabeled corpora"
    - "echoes of power language effects and power differences in social interaction"

Connected through hyperedge HE-4 (induced by source paper "event2mind commonsense inference on events intents and reactions"):
  with members:
    - "analyzing gender stereotyping in bollywood movies"
    - "detecting and explaining causes from text for a time series event"
    - "learning to paraphrase for question answering"
    - "a question answering approach to emotion cause extraction"
    - "ordinal common sense inference"
    - "generating sentences from a continuous space"
    - "a large annotated corpus for learning natural language inference"
    - "on the properties of neural machine translation encoder decoder approaches"

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)