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 "faster shift reduce constituent parsing with a non binary bottom up strategy"):
  Abstract: Abstract   An increasingly wide range of artificial intelligence applications rely on syntactic information to process and extract meaning from natural language text or speech, with constituent trees being one of the most widely used syntactic formalisms. To produce these phrase-structure representations from sentences in natural language, shift-reduce constituent parsers have become one of the most efficient approaches. Increasing their accuracy and speed is still one of the main objectives pursued by the research community so that artificial intelligence applications that make use of parsing outputs, such as machine translation or voice assistant services, can improve their performance. With this goal in mind, we propose in this article a novel non-binary shift-reduce algorithm for constituent parsing. Our parser follows a classical bottom-up strategy but, unlike others, it straightforwardly creates non-binary branchings with just one    Reduce    transition, instead of requiring prior binarization or a sequence of binary transitions, allowing its direct application to any language without the need of further resources such as percolation tables. As a result, it uses fewer transitions per sentence than existing transition-based constituent parsers, becoming the fastest such system and, as a consequence, speeding up downstream applications. Using static oracle training and greedy search, the accuracy of this novel approach is on par with state-of-the-art transition-based constituent parsers and outperforms all top-down and bottom-up greedy shift-reduce systems on the Wall Street Journal section from the English Penn Treebank and the Penn Chinese Treebank. Additionally, we develop a dynamic oracle for training the proposed transition-based algorithm, achieving further improvements in both benchmarks and obtaining the best accuracy to date on the Penn Chinese Treebank among greedy shift-reduce parsers.

Includes members:
  Member "what s going on in neural constituency parsers an analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "linguistic knowledge and transferability of contextual representations")
  Member "syntactic scaffolds for semantic structures":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generalizing natural language analysis through span relation representations")
  Member "a tree based decoder for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unsupervised program synthesis for images using tree structured lstm")
  Member "constituency parsing with a self attentive encoder":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "are pre trained language models aware of phrases simple but strong baselines for grammar induction")
  Member "efficient dependency guided named entity recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dependency guided lstm crf for named entity recognition")
  Member "in order transition based constituent parsing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "head driven phrase structure grammar parsing on penn treebank")
  Member "a minimal span based neural constituency parser":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ecsp a new task for emotion cause span pair extraction and classification")
  Member "towards string to tree neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "structural language models of code")

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)