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: "multitask parsing across semantic representations"
  Abstract: The ability to consolidate information of different types is at the core of intelligence, and has tremendous practical value in allowing learning for one task to benefit from generalizations learned for others. In this paper we tackle the challenging task of improving semantic parsing performance, taking UCCA parsing as a test case, and AMR, SDP and Universal Dependencies (UD) parsing as auxiliary tasks. We experiment on three languages, using a uniform transition-based system and learning architecture for all parsing tasks. Despite notable conceptual, formal and domain differences, we show that multitask learning significantly improves UCCA parsing in both in-domain and out-of-domain settings.

Connected through hyperedge HE-1 (induced by source paper "broad coverage semantic parsing as transduction"):
  with members:
    - "core semantic first a top down approach for amr parsing"
    - "compositional semantic parsing across graphbanks"
    - "semeval 2019 task 1 cross lingual semantic parsing with ucca"
    - "rewarding smatch transition based amr parsing with reinforcement learning"
    - "hlt suda at semeval 2019 task 1 ucca graph parsing as constituent tree parsing"
    - "learning joint semantic parsers from disjoint data"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "amr parsing as graph prediction with latent alignment"

Connected through hyperedge HE-2 (induced by source paper "sequence labeling parsing by learning across representations"):
  with members:
    - "viable dependency parsing as sequence labeling"
    - "better faster stronger sequence tagging constituent parsers"
    - "left to right dependency parsing with pointer networks"
    - "constituent parsing as sequence labeling"
    - "zero shot sequence labeling transferring knowledge from sentences to tokens"
    - "ncrf an open source neural sequence labeling toolkit"
    - "constituency parsing with a self attentive encoder"
    - "identifying beneficial task relations for multi task learning in deep neural networks"

Connected through hyperedge HE-3 (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-4 (induced by source paper "compositional semantic parsing across graphbanks"):
  with members:
    - "amr parsing as sequence to graph transduction"
    - "learning joint semantic parsers from disjoint data"
    - "parser training with heterogeneous treebanks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "amr dependency parsing with a typed semantic algebra"
    - "amr parsing as graph prediction with latent alignment"
    - "simpler but more accurate semantic dependency parsing"
    - "robust incremental neural semantic graph parsing"

Connected through hyperedge HE-5 (induced by source paper "semeval 2019 task 1 cross lingual semantic parsing with ucca"):
  with members:
    - "hlt suda at semeval 2019 task 1 ucca graph parsing as constituent tree parsing"
    - "toward abstractive summarization using semantic representations"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "simple and effective text simplification using semantic and neural methods"
    - "a transition based directed acyclic graph parser for ucca"
    - "a minimal span based neural constituency parser"
    - "text summarization using abstract meaning representation"

Connected through hyperedge HE-6 (induced by source paper "ufal mrpipe at mrp 2019 udpipe goes semantic in the meaning representation parsing shared task"):
  with members:
    - "evaluating contextualized embeddings on 54 languages in pos tagging lemmatization and dependency parsing"
    - "semeval 2019 task 1 cross lingual semantic parsing with ucca"
    - "tensorflow eager a multi stage python embedded dsl for machine learning"
    - "udpipe at sigmorphon 2019 contextualized embeddings regularization with morphological categories corpora merging"
    - "amr parsing as sequence to graph transduction"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "advances in pre training distributed word representations"
    - "finding function in form compositional character models for open vocabulary word representation"

Connected through hyperedge HE-7 (induced by source paper "on the limits of learning to actively learn semantic representations"):
  with members:
    - "practical obstacles to deploying active learning"
    - "allennlp a deep semantic natural language processing platform"
    - "large scale qa srl parsing"
    - "breaking the beam search curse a study of re scoring methods and stopping criteria for neural machine translation"
    - "deep bayesian active learning for natural language processing results of a large scale empirical study"
    - "learning how to active learn a deep reinforcement learning approach"
    - "learning active learning from data"
    - "learning algorithms for active learning"

Connected through hyperedge HE-8 (induced by source paper "hitachi at mrp 2019 unified encoder to biaffine network for cross framework meaning representation parsing"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "amr parsing as graph prediction with latent alignment"
    - "simpler but more accurate semantic dependency parsing"
    - "a transition based directed acyclic graph parser for ucca"
    - "attention is all you need"
    - "neural amr sequence to sequence models for parsing and generation"
    - "chainermn scalable distributed deep learning framework"

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)