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: "empath understanding topic signals in large scale text"
  Abstract: Human language is colored by a broad range of topics, but existing text analysis tools only focus on a small number of them. We present Empath, a tool that can generate and validate new lexical categories on demand from a small set of seed terms (like "bleed" and "punch" to generate the category violence). Empath draws connotations between words and phrases by deep learning a neural embedding across more than 1.8 billion words of modern fiction. Given a small set of seed words that characterize a category, Empath uses its neural embedding to discover new related terms, then validates the category with a crowd-powered filter. Empath also analyzes text across 200 built-in, pre-validated categories we have generated from common topics in our web dataset, like neglect, government, and social media. We show that Empath's data-driven, human validated categories are highly correlated (r=0.906) with similar categories in LIWC.

Connected through hyperedge HE-1 (induced by source paper "ten social dimensions of conversations and relationships"):
  with members:
    - "firsthand opiates abuse on social media monitoring geospatial patterns of interest through a digital cohort"
    - "the language of dialogue is complex"
    - "coloring in the links capturing social ties as they are perceived"
    - "the anatomy of reddit an overview of academic research"
    - "community interaction and conflict on the web"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "caveat emptor computational social science large scale missing data in a widely published reddit corpus"
    - "automated hate speech detection and the problem of offensive language"

Connected through hyperedge HE-2 (induced by source paper "a unified system for aggression identification in english code mixed and uni lingual texts"):
  with members:
    - "hierarchical taxonomy aware and attentional graph capsule rcnns for large scale multi label text classification"
    - "higru hierarchical gated recurrent units for utterance level emotion recognition"
    - "towards generating long and coherent text with multi level latent variable models"
    - "mattnet modular attention network for referring expression comprehension"
    - "universal language model fine tuning for text classification"
    - "lstms with attention for aggression detection"
    - "ritual uh at trac 2018 shared task aggression identification"
    - "aggression annotated corpus of hindi english code mixed data"

Connected through hyperedge HE-3 (induced by source paper "aggressionnet generalised multi modal deep temporal and sequential learning for aggression identification"):
  with members:
    - "hierarchical taxonomy aware and attentional graph capsule rcnns for large scale multi label text classification"
    - "higru hierarchical gated recurrent units for utterance level emotion recognition"
    - "towards generating long and coherent text with multi level latent variable models"
    - "mattnet modular attention network for referring expression comprehension"
    - "lstms with attention for aggression detection"
    - "ritual uh at trac 2018 shared task aggression identification"
    - "aggression annotated corpus of hindi english code mixed data"
    - "advances in pre training distributed word representations"

Connected through hyperedge HE-4 (induced by source paper "quantifying engagement with citations on wikipedia"):
  with members:
    - "citation needed a taxonomy and algorithmic assessment of wikipedia s verifiability"
    - "mapping the backbone of the humanities through the eyes of wikipedia"
    - "detecting and gauging impact on wikipedia page views"
    - "eliciting new wikipedia users interests via automatically mined questionnaires for a warm welcome not a cold start"
    - "why the world reads wikipedia beyond english speakers"
    - "structuring wikipedia articles with section recommendations"
    - "amplifying the impact of open access wikipedia and the diffusion of science"
    - "why we read wikipedia"

Connected through hyperedge HE-5 (induced by source paper "stre self attentive edit quality prediction in wikipedia"):
  with members:
    - "neural based statement classification for biased language"
    - "universal language model fine tuning for text classification"
    - "distilling information reliability and source trustworthiness from digital traces"
    - "a structured self attentive sentence embedding"
    - "building automated vandalism detection tools for wikidata"
    - "wikipedia vandal early detection from user behavior to user embedding"
    - "end to end memory networks"
    - "learning transferable features with deep adaptation networks"

Connected through hyperedge HE-6 (induced by source paper "variational spectral graph convolutional networks"):
  with members:
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "bayesian semi supervised learning with graph gaussian processes"
    - "like sheep among wolves characterizing hateful users on twitter"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "semi supervised classification with graph convolutional networks"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

Connected through hyperedge HE-7 (induced by source paper "subjective databases"):
  with members:
    - "voyageur an experiential travel search engine"
    - "deep learning for sentiment analysis a survey"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "learning a neural semantic parser from user feedback"
    - "recursive neural conditional random fields for aspect based sentiment analysis"
    - "inducing domain specific sentiment lexicons from unlabeled corpora"

Connected through hyperedge HE-8 (induced by source paper "race ethnicity and national origin based discrimination in social media and hate crimes across 100 u s cities"):
  with members:
    - "the effect of extremist violence on hateful speech online"
    - "socio spatial self organizing maps using social media to assess relevant geographies for exposure to social processes"
    - "peer to peer hate hate speech instigators and their targets"
    - "hate lingo a target based linguistic analysis of hate speech in social media"
    - "characterizing and detecting hateful users on twitter"
    - "the paradigm shift of social spambots evidence theories and tools for the arms race"
    - "online human bot interactions detection estimation and characterization"
    - "ex machina personal attacks seen 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)