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: "representing meaning with a combination of logical and distributional models"
  Abstract: NLP tasks differ in the semantic information they require, and at this time no single se- mantic representation fulfills all requirements. Logic-based representations characterize sentence structure, but do not capture the graded aspect of meaning. Distributional models give graded similarity ratings for words and phrases, but do not capture sentence structure in the same detail as logic-based approaches. So it has been argued that the two are complementary. We adopt a hybrid approach that combines logic-based and distributional semantics through probabilistic logic inference in Markov Logic Networks (MLNs). In this paper, we focus on the three components of a practical system integrating logical and distributional models: 1) Parsing and task representation is the logic-based part where input problems are represented in probabilistic logic. This is quite different from representing them in standard first-order logic. 2) For knowledge base construction we form weighted inference rules. We integrate and compare distributional information with other sources, notably WordNet and an existing paraphrase collection. In particular, we use our system to evaluate distributional lexical entailment approaches. We use a variant of Robinson resolution to determine the necessary inference rules. More sources can easily be added by mapping them to logical rules; our system learns a resource-specific weight that corrects for scaling differences between resources. 3) In discussing probabilistic inference, we show how to solve the inference problems efficiently. To evaluate our approach, we use the task of textual entailment (RTE), which can utilize the strengths of both logic-based and distributional representations. In particular we focus on the SICK dataset, where we achieve state-of-the-art results.

Connected through hyperedge HE-1 (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"
    - "universal semantic parsing"

Connected through hyperedge HE-2 (induced by source paper "monalog a lightweight system for natural language inference based on monotonicity"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "probing natural language inference models through semantic fragments"
    - "annotation artifacts in natural language inference data"
    - "acquisition of phrase correspondences using natural deduction proofs"
    - "hypothesis only baselines in natural language inference"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "stress test evaluation for natural language inference"
    - "supervised learning of universal sentence representations from natural language inference data"

Connected through hyperedge HE-3 (induced by source paper "a structured distributional model of sentence meaning and processing"):
  with members:
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "measuring thematic fit with distributional feature overlap"
    - "question answering with grammatically interpretable representations"
    - "the mechanism of additive composition"
    - "is structure necessary for modeling argument expectations in distributional semantics"
    - "fine grained analysis of sentence embeddings using auxiliary prediction tasks"
    - "representing verbs with rich contexts an evaluation on verb similarity"
    - "learning distributed representations of sentences from unlabelled data"

Connected through hyperedge HE-4 (induced by source paper "adventure adversarial training for textual entailment with knowledge guided examples"):
  with members:
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "annotation artifacts in natural language inference data"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "deep contextualized word representations"
    - "detecting and explaining causes from text for a time series event"
    - "catgan coupled adversarial transfer for domain generation"
    - "natural language inference over interaction space"
    - "triple generative adversarial nets"

Connected through hyperedge HE-5 (induced by source paper "acquisition of phrase correspondences using natural deduction proofs"):
  with members:
    - "toward abstractive summarization using semantic representations"
    - "a ccg parsing with a supertag and dependency factored model"
    - "determining semantic textual similarity using natural deduction proofs"
    - "task specific attentive pooling of phrase alignments contributes to sentence matching"
    - "learning semantically and additively compositional distributional representations"
    - "a large annotated corpus for learning natural language inference"

Connected through hyperedge HE-6 (induced by source paper "end to end differentiable proving"):
  with members:
    - "holstep a machine learning dataset for higher order logic theorem proving"
    - "reasonet learning to stop reading in machine comprehension"
    - "harnessing deep neural networks with logic rules"
    - "programming with a differentiable forth interpreter"
    - "chains of reasoning over entities relations and text using recurrent neural networks"
    - "lifted rule injection for relation embeddings"
    - "learning to compose neural networks for question answering"
    - "separating answers from queries for neural reading comprehension"

Connected through hyperedge HE-7 (induced by source paper "semantic composition via probabilistic model theory"):
  with members:
    - "sentence entailment in compositional distributional semantics"
    - "variational inference for logical inference"
    - "functional distributional semantics"
    - "simverb 3500 a large scale evaluation set of verb similarity"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "efficient estimation of word representations in vector space"
    - "multi step regression learning for compositional distributional semantics"
    - "experimental support for a categorical compositional distributional model of meaning"

Connected through hyperedge HE-8 (induced by source paper "variational inference for logical inference"):
  with members:
    - "semantic composition via probabilistic model theory"
    - "functional distributional semantics"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "efficient estimation of word representations in vector space"
    - "expectation propagation for approximate bayesian inference"
    - "from frequency to meaning vector space models of semantics"
    - "mathematical foundations for a compositional distributional model of meaning"

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)