human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a syntax aware multi task learning framework for chinese semantic role labeling"):
  Abstract: Semantic role labeling (SRL) aims to identify the predicate-argument structure of a sentence. Inspired by the strong correlation between syntax and semantics, previous works pay much attention to improve SRL performance on exploiting syntactic knowledge, achieving significant results. Pipeline methods based on automatic syntactic trees and multi-task learning (MTL) approaches using standard syntactic trees are two common research orientations. In this paper, we adopt a simple unified span-based model for both span-based and word-based Chinese SRL as a strong baseline. Besides, we present a MTL framework that includes the basic SRL module and a dependency parser module. Different from the commonly used hard parameter sharing strategy in MTL, the main idea is to extract implicit syntactic representations from the dependency parser as external inputs for the basic SRL model. Experiments on the benchmarks of Chinese Proposition Bank 1.0 and CoNLL-2009 Chinese datasets show that our proposed framework can effectively improve the performance over the strong baselines. With the external BERT representations, our framework achieves new state-of-the-art 87.54 and 88.5 F1 scores on the two test data of the two benchmarks, respectively. In-depth analysis are conducted to gain more insights on the proposed framework and the effectiveness of syntax.

Includes members:
  Member "dependency or span end to end uniform semantic role labeling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "semantics aware bert for language understanding")
  Member "syntax enhanced neural machine translation with syntax aware word representations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "is pos tagging necessary or even helpful for neural dependency parsing")
  Member "syntax aware neural semantic role labeling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "how to best use syntax in semantic role labelling")
  Member "syntactic scaffolds for semantic structures":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generalizing natural language analysis through span relation representations")
  Member "learning joint semantic parsers from disjoint data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "text generation with exemplar based adaptive decoding")
  Member "a span selection model for semantic role labeling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "span based joint entity and relation extraction with transformer pre training")
  Member "jointly predicting predicates and arguments in neural semantic role labeling":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "spanbert improving pre training by representing and predicting spans")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")

Question: Please tell me which class the source paper 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)