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: "computing lexical contrast"
  Abstract: Knowing the degree of semantic contrast between words has widespread application in natural language processing, including machine translation, information retrieval, and dialogue systems. Manually-created lexicons focus on opposites, such as {\rm hot} and {\rm cold}. Opposites are of many kinds such as antipodals, complementaries, and gradable. However, existing lexicons often do not classify opposites into the different kinds. They also do not explicitly list word pairs that are not opposites but yet have some degree of contrast in meaning, such as {\rm warm} and {\rm cold} or {\rm tropical} and {\rm freezing}. We propose an automatic method to identify contrasting word pairs that is based on the hypothesis that if a pair of words, $A$ and $B$, are contrasting, then there is a pair of opposites, $C$ and $D$, such that $A$ and $C$ are strongly related and $B$ and $D$ are strongly related. (For example, there exists the pair of opposites {\rm hot} and {\rm cold} such that {\rm tropical} is related to {\rm hot,} and {\rm freezing} is related to {\rm cold}.) We will call this the contrast hypothesis. We begin with a large crowdsourcing experiment to determine the amount of human agreement on the concept of oppositeness and its different kinds. In the process, we flesh out key features of different kinds of opposites. We then present an automatic and empirical measure of lexical contrast that relies on the contrast hypothesis, corpus statistics, and the structure of a {\it Roget}-like thesaurus. We show that the proposed measure of lexical contrast obtains high precision and large coverage, outperforming existing methods.

Connected through hyperedge HE-1 (induced by source paper "from f to a on the n y regents science exams an overview of the aristo project"):
  with members:
    - "mathqa towards interpretable math word problem solving with operation based formalisms"
    - "quartz an open domain dataset of qualitative relationship questions"
    - "repurposing entailment for multi hop question answering tasks"
    - "roberta a robustly optimized bert pretraining approach"
    - "annotation artifacts in natural language inference data"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "hypothesis only baselines in natural language inference"

Connected through hyperedge HE-2 (induced by source paper "antonym synonym classification based on new sub space embeddings"):
  with members:
    - "one shot and few shot learning of word embeddings"
    - "deep contextualized word representations"
    - "distinguishing antonyms and synonyms in a pattern based neural network"
    - "fasttext zip compressing text classification models"
    - "specialising word vectors for lexical entailment"
    - "poincar e embeddings for learning hierarchical representations"
    - "xgboost a scalable tree boosting system"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "interpreting verbal irony linguistic strategies and the connection to thetype of semantic incongruity"):
  with members:
    - "exploring author context for detecting intended vs perceived sarcasm"
    - "towards multimodal sarcasm detection an _obviously_ perfect paper"
    - "sarcasm analysis using conversation context"
    - "with 1 follower i must be awesome p exploring the role of irony markers in irony recognition"
    - "cascade contextual sarcasm detection in online discussion forums"
    - "sarcasm sign interpreting sarcasm with sentiment based monolingual machine translation"
    - "harnessing cognitive features for sarcasm detection"
    - "the role of conversation context for sarcasm detection in online interactions"

Connected through hyperedge HE-4 (induced by source paper "generalizing procrustes analysis for better bilingual dictionary induction"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "improving cross lingual word embeddings by meeting in the middle"
    - "word translation without parallel data"
    - "cross lingual induction and transfer of verb classes based on word vector space specialisation"
    - "massively multilingual word embeddings"
    - "exploiting similarities among languages for machine translation"

Connected through hyperedge HE-5 (induced by source paper "behavior analysis of nli models uncovering the influence of three factors on robustness"):
  with members:
    - "a challenge set approach to evaluating machine translation"
    - "adversarial examples for evaluating reading comprehension systems"
    - "a decomposable attention model for natural language inference"
    - "enhanced lstm for natural language inference"
    - "understanding neural networks through representation erasure"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"

Connected through hyperedge HE-6 (induced by source paper "path based vs distributional information in recognizing lexical semantic relations"):
  with members:
    - "nine features in a random forest to learn taxonomical semantic relations"
    - "improving hypernymy detection with an integrated path based and distributional method"
    - "learning distributed word representations for natural logic reasoning"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "cogalex v shared task root18"):
  with members:
    - "relations such as hypernymy identifying and exploiting hearst patterns in distributional vectors for lexical entailment"
    - "nine features in a random forest to learn taxonomical semantic relations"
    - "a uniform approach to analogies synonyms antonyms and associations"

Connected through hyperedge HE-8 (induced by source paper "integrating distributional lexical contrast into word embeddings for antonym synonym distinction"):
  with members:
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "efficient estimation of word representations in vector space"
    - "from frequency to meaning vector space models of semantics"

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)