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: "a transition based directed acyclic graph parser for ucca"
  Abstract: We present the first parser for UCCA, a cross-linguistically applicable framework for semantic representation, which builds on extensive typological work and supports rapid annotation. UCCA poses a challenge for existing parsing techniques, as it exhibits reentrancy (resulting in DAG structures), discontinuous structures and non-terminal nodes corresponding to complex semantic units. To our knowledge, the conjunction of these formal properties is not supported by any existing parser. Our transition-based parser, which uses a novel transition set and features based on bidirectional LSTMs, has value not just for UCCA parsing: its ability to handle more general graph structures can inform the development of parsers for other semantic DAG structures, and in languages that frequently use discontinuous structures.

Connected through hyperedge HE-1 (induced by source paper "a comparative analysis of knowledge intensive and data intensive semantic parsers"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "a challenge set for french english machine translation"
    - "analysis methods in neural language processing a survey"
    - "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "amr dependency parsing with a typed semantic algebra"

Connected through hyperedge HE-2 (induced by source paper "deep contextualized self training for low resource dependency parsing"):
  with members:
    - "viable dependency parsing as sequence labeling"
    - "a survey on recent advances in named entity recognition from deep learning models"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "no training required exploring random encoders for sentence classification"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "strong baselines for neural semi supervised learning under domain shift"
    - "distant supervision from disparate sources for low resource part of speech tagging"
    - "can you tell me how to get past sesame street sentence level pretraining beyond language modeling"

Connected through hyperedge HE-3 (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"
    - "multitask parsing across semantic representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (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-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"
    - "multitask parsing across 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 minimal span based neural constituency parser"
    - "text summarization using abstract meaning representation"

Connected through hyperedge HE-6 (induced by source paper "multi task semantic dependency parsing with policy gradient for learning easy first strategies"):
  with members:
    - "backpropagating through structured argmax using a spigot"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "learning joint semantic parsers from disjoint data"
    - "policy gradient as a proxy for dynamic oracles in constituency parsing"
    - "simpler but more accurate semantic dependency parsing"
    - "deep multitask learning for semantic dependency parsing"
    - "recurrent neural network grammars"
    - "a joint many task model growing a neural network for multiple nlp tasks"

Connected through hyperedge HE-7 (induced by source paper "hitachi at mrp 2019 unified encoder to biaffine network for cross framework meaning representation parsing"):
  with members:
    - "multitask parsing across semantic representations"
    - "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"
    - "attention is all you need"
    - "neural amr sequence to sequence models for parsing and generation"
    - "chainermn scalable distributed deep learning framework"

Connected through hyperedge HE-8 (induced by source paper "content differences in syntactic and semantic representations"):
  with members:
    - "syntactic scaffolds for semantic structures"
    - "universal dependency parsing with a general transition based dag parser"
    - "multitask parsing across semantic representations"
    - "simple and effective text simplification using semantic and neural methods"
    - "linguistically informed self attention for semantic role labeling"
    - "hume human ucca based evaluation of machine translation"
    - "an incremental parser for abstract meaning representation"
    - "getting more out of syntax with props"

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)