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: "universal semantic parsing"
  Abstract: Universal Dependencies (UD) offer a uniform cross-lingual syntactic representation, with the aim of advancing multilingual applications. Recent work shows that semantic parsing can be accomplished by transforming syntactic dependencies to logical forms. However, this work is limited to English, and cannot process dependency graphs, which allow handling complex phenomena such as control. In this work, we introduce UDepLambda, a semantic interface for UD, which maps natural language to logical forms in an almost language-independent fashion and can process dependency graphs. We perform experiments on question answering against Freebase and provide German and Spanish translations of the WebQuestions and GraphQuestions datasets to facilitate multilingual evaluation. Results show that UDepLambda outperforms strong baselines across languages and datasets. For English, it achieves a 4.9 F1 point improvement over the state-of-the-art on GraphQuestions. Our code and data can be downloaded at this https URL.

Connected through hyperedge HE-1 (induced by source paper "reasoning driven question answering for natural language understanding"):
  with members:
    - "on the capabilities and limitations of reasoning for natural language understanding"
    - "evidence sentence extraction for machine reading comprehension"
    - "question answering as global reasoning over semantic abstractions"
    - "going on a vacation takes longer than going for a walk a study of temporal commonsense understanding"
    - "cogcomptime a tool for understanding time in natural language text"
    - "annotation artifacts in natural language inference data"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "multi hop knowledge graph reasoning with reward shaping"

Connected through hyperedge HE-2 (induced by source paper "eat2seq a generic framework for controlled sentence transformation without task specific training"):
  with members:
    - "gromov wasserstein alignment of word embedding spaces"
    - "graph to sequence learning using gated graph neural networks"
    - "style transfer through back translation"
    - "a graph to sequence model for amr to text generation"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "phrase based neural unsupervised machine translation"
    - "unsupervised machine translation using monolingual corpora only"

Connected through hyperedge HE-3 (induced by source paper "a survey of cross lingual features for zero shot cross lingual semantic parsing"):
  with members:
    - "polyglot semantic role labeling"
    - "cross lingual semantic parsing"
    - "exploring neural methods for parsing discourse representation structures"
    - "evaluating scoped meaning representations"
    - "coarse to fine decoding for neural semantic parsing"
    - "neural networks for cross lingual negation scope detection"
    - "cross lingual abstract meaning representation parsing"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "bidirectional attentive memory networks for question answering over knowledge bases"):
  with members:
    - "exploiting rich syntactic information for semantic parsing with graph to sequence model"
    - "graph2seq graph to sequence learning with attention based neural networks"
    - "question answering on knowledge bases and text using universal schema and memory networks"
    - "neural symbolic machines learning semantic parsers on freebase with weak supervision"
    - "bidirectional attention flow for machine comprehension"
    - "s mart novel tree based structured learning algorithms applied to tweet entity linking"
    - "key value memory networks for directly reading documents"
    - "dynamic coattention networks for question answering"

Connected through hyperedge HE-5 (induced by source paper "a hybrid semantic parsing approach for tabular data analysis"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "the natural language decathlon multitask learning as question answering"
    - "semantic parsing with syntax and table aware sql generation"
    - "typesql knowledge based type aware neural text to sql generation"
    - "learning to compute word embeddings on the fly"
    - "mimicking word embeddings using subword rnns"
    - "a minimal span based neural constituency parser"

Connected through hyperedge HE-6 (induced by source paper "on the capabilities and limitations of reasoning for natural language understanding"):
  with members:
    - "question answering as global reasoning over semantic abstractions"
    - "multi hop knowledge graph reasoning with reward shaping"
    - "worldtree a corpus of explanation graphs for elementary science questions supporting multi hop inference"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "answering complex questions using open information extraction"
    - "question answering via integer programming over semi structured knowledge"
    - "chains of reasoning over entities relations and text using recurrent neural networks"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-7 (induced by source paper "vasta a vision and language assisted smartphone task automation system"):
  with members:
    - "statelens a reverse engineering solution for making existing dynamic touchscreens accessible"
    - "pumice a multi modal agent that learns concepts and conditionals from natural language and demonstrations"
    - "yolov3 an incremental improvement"
    - "universal sentence encoder"
    - "focal loss for dense object detection"
    - "towards zero shot frame semantic parsing for domain scaling"
    - "frame semantic parsing with softmax margin segmental rnns and a syntactic scaffold"
    - "learning to map sentences to logical form structured classification with probabilistic categorial grammars"

Connected through hyperedge HE-8 (induced by source paper "weakly supervised neural semantic parsing with a generative ranker"):
  with members:
    - "structvae tree structured latent variable models for semi supervised semantic parsing"
    - "question answering on knowledge bases and text using universal schema and memory networks"
    - "weakly supervised semantic parsing with abstract examples"
    - "learning a neural semantic parser from user feedback"
    - "a generative parser with a discriminative recognition algorithm"
    - "language to logical form with neural attention"
    - "neural symbolic machines learning semantic parsers on freebase with weak supervision"
    - "learning executable semantic parsers for natural language understanding"

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)