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: "towards universal semantic tagging"
  Abstract: The paper proposes the task of universal semantic tagging---tagging word tokens with language-neutral, semantically informative tags. We argue that the task, with its independent nature, contributes to better semantic analysis for wide-coverage multilingual text. We present the initial version of the semantic tagset and show that (a) the tags provide semantically fine-grained information, and (b) they are suitable for cross-lingual semantic parsing. An application of the semantic tagging in the Parallel Meaning Bank supports both of these points as the tags contribute to formal lexical semantics and their cross-lingual projection. As a part of the application, we annotate a small corpus with the semantic tags and present new baseline result for universal semantic tagging.

Connected through hyperedge HE-1 (induced by source paper "help a dataset for identifying shortcomings of neural models in monotonicity reasoning"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "collecting diverse natural language inference problems for sentence representation evaluation"
    - "hypothesis only baselines in natural language inference"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "stress testing neural models of natural language inference with multiply quantified sentences"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "performance impact caused by hidden bias of training data for recognizing textual entailment"

Connected through hyperedge HE-2 (induced by source paper "semantic graph parsing with recurrent neural network dag grammars"):
  with members:
    - "exploring neural methods for parsing discourse representation structures"
    - "learning deep generative models of graphs"
    - "sequence to action end to end semantic graph generation for semantic parsing"
    - "amr dependency parsing with a typed semantic algebra"
    - "amr parsing as graph prediction with latent alignment"
    - "robust incremental neural semantic graph parsing"
    - "the parallel meaning bank towards a multilingual corpus of translations annotated with compositional meaning representations"
    - "incorporating copying mechanism in sequence to sequence learning"

Connected through hyperedge HE-3 (induced by source paper "decomposing generalization models of generic habitual and episodic statements"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural models of factuality"
    - "commonsense for generative multi hop question answering tasks"
    - "ordinal common sense inference"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "the universal decompositional semantics dataset and decomp toolkit"):
  with members:
    - "decomposing generalization models of generic habitual and episodic statements"
    - "fine grained temporal relation extraction"
    - "comprehensive supersense disambiguation of english prepositions and possessives"
    - "neural models of factuality"
    - "the parallel meaning bank towards a multilingual corpus of translations annotated with compositional meaning representations"

Connected through hyperedge HE-5 (induced by source paper "what can we learn from semantic tagging"):
  with members:
    - "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks"
    - "the parallel meaning bank towards a multilingual corpus of translations annotated with compositional meaning representations"
    - "beyond shared hierarchies deep multitask learning through soft layer ordering"
    - "semantic tagging with deep residual networks"
    - "fully adaptive feature sharing in multi task networks with applications in person attribute classification"
    - "cross stitch networks for multi task learning"
    - "enhanced lstm for natural language inference"
    - "deep biaffine attention for neural dependency parsing"

Connected through hyperedge HE-6 (induced by source paper "evaluating scoped meaning representations"):
  with members:
    - "neural semantic parsing by character based translation experiments with abstract meaning representations"
    - "the parallel meaning bank towards a multilingual corpus of translations annotated with compositional meaning representations"
    - "semantic tagging with deep residual networks"

Connected through hyperedge HE-7 (induced by source paper "comprehensive supersense disambiguation of english prepositions and possessives"):
  with members:
    - "adposition supersenses v2"
    - "semi supervised preposition sense disambiguation using multilingual data"
    - "distributed representations of words and phrases and their compositionality"

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)