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: "comprehensive supersense disambiguation of english prepositions and possessives"
  Abstract: Semantic relations are often signaled with prepositional or possessive marking--but extreme polysemy bedevils their analysis and automatic interpretation. We introduce a new annotation scheme, corpus, and task for the disambiguation of prepositions and possessives in English. Unlike previous approaches, our annotations are comprehensive with respect to types and tokens of these markers; use broadly applicable supersense classes rather than fine-grained dictionary definitions; unite prepositions and possessives under the same class inventory; and distinguish between a marker's lexical contribution and the role it marks in the context of a predicate or scene. Strong interannotator agreement rates, as well as encouraging disambiguation results with established supervised methods, speak to the viability of the scheme and task.

Connected through hyperedge HE-1 (induced by source paper "a corpus of adpositional supersenses for mandarin chinese"):
  with members:
    - "universal dependency parsing from scratch"
    - "adpositional supersenses for mandarin chinese"
    - "adposition supersenses v2"

Connected through hyperedge HE-2 (induced by source paper "linguistic knowledge and transferability of contextual representations"):
  with members:
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "what can linguistics and deep learning contribute to each other"
    - "what do rnn language models learn about filler gap dependencies"
    - "what s going on in neural constituency parsers an analysis"
    - "language modeling teaches you more syntax than translation does lessons learned through auxiliary task analysis"
    - "why self attention a targeted evaluation of neural machine translation architectures"

Connected through hyperedge HE-3 (induced by source paper "probing what different nlp tasks teach machines about function word comprehension"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "on the convergence of adam and beyond"
    - "under the hood using diagnostic classifiers to investigate and improve how language models track agreement information"
    - "the natural language decathlon multitask learning as question answering"
    - "on the evaluation of semantic phenomena in neural machine translation using natural language inference"
    - "collecting diverse natural language inference problems for sentence representation evaluation"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "allennlp a deep semantic natural language processing platform"

Connected through hyperedge HE-4 (induced by source paper "shallow syntax in deep water"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "linguistic knowledge and transferability of contextual representations"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deep rnns encode soft hierarchical syntax"
    - "neural models of factuality"
    - "universal language model fine tuning for text classification"

Connected through hyperedge HE-5 (induced by source paper "made for each other broad coverage semantic structures meet preposition supersenses"):
  with members:
    - "semeval 2019 task 1 cross lingual semantic parsing with ucca"
    - "on the convergence of adam and beyond"
    - "hlt suda at semeval 2019 task 1 ucca graph parsing as constituent tree parsing"
    - "semantically constrained multilayer annotation the case of coreference"
    - "linguistic knowledge and transferability of contextual representations"
    - "semantic structural evaluation for text simplification"
    - "embedding syntax and semantics of prepositions via tensor decomposition"
    - "syntactic scaffolds for semantic structures"

Connected through hyperedge HE-6 (induced by source paper "the universal decompositional semantics dataset and decomp toolkit"):
  with members:
    - "decomposing generalization models of generic habitual and episodic statements"
    - "fine grained temporal relation extraction"
    - "neural models of factuality"
    - "towards universal semantic tagging"
    - "the parallel meaning bank towards a multilingual corpus of translations annotated with compositional meaning representations"

Connected through hyperedge HE-7 (induced by source paper "distributional analysis of function words"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "from frequency to meaning vector space models of semantics"
    - "learning concept hierarchies from text corpora using formal concept analysis"

Connected through hyperedge HE-8 (induced by source paper "adposition and case supersenses v2 4 guidelines for english"):
  with members:
    - "an inventory of preposition relations"

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)