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: "interpretable semantic textual similarity"
  Abstract: We address interpretability, the ability of machines to explain their reasoning.We formalize it for textual similarity as graded typed alignment between 2 sentences.We release an annotated dataset and build and evaluate a high performance system.We show that the output of the system can be used to produce explanations.2 user studies show preliminary evidence that explanations help humans perform better. User acceptance of artificial intelligence agents might depend on their ability to explain their reasoning to the users. We focus on a specific text processing task, the Semantic Textual Similarity task (STS), where systems need to measure the degree of semantic equivalence between two sentences. We propose to add an interpretability layer (iSTS for short) formalized as the alignment between pairs of segments across the two sentences, where the relation between the segments is labeled with a relation type and a similarity score. This way, a system performing STS could use the interpretability layer to explain to users why it returned that specific score for the given sentence pair. We present a publicly available dataset of sentence pairs annotated following the formalization. We then develop an iSTS system trained on this dataset, which given a sentence pair finds what is similar and what is different, in the form of graded and typed segment alignments. When evaluated on the dataset, the system performs better than an informed baseline, showing that the dataset and task are well-defined and feasible. Most importantly, two user studies show how the iSTS system output can be used to automatically produce explanations in natural language. Users performed the two tasks better when having access to the explanations, providing preliminary evidence that our dataset and method to automatically produce explanations do help users understand the output of STS systems better.

Connected through hyperedge HE-1 (induced by source paper "playing log n questions over sentences"):
  with members:
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "learning to describe differences between pairs of similar images"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "learning to disambiguate by asking discriminative questions"
    - "constructing datasets for multi hop reading comprehension across documents"
    - "end to end memory networks"
    - "a large annotated corpus for learning natural language inference"
    - "estimating or propagating gradients through stochastic neurons for conditional computation"

Connected through hyperedge HE-2 (induced by source paper "a deep network model for paraphrase detection in short text messages"):
  with members:
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "neural paraphrase identification of questions with noisy pretraining"
    - "revisiting recurrent networks for paraphrastic sentence embeddings"
    - "bilateral multi perspective matching for natural language sentences"
    - "sentence similarity learning by lexical decomposition and composition"
    - "enriching word vectors with subword information"
    - "skip thought vectors"
    - "abcnn attention based convolutional neural network for modeling sentence pairs"

Connected through hyperedge HE-3 (induced by source paper "a resource light method for cross lingual semantic textual similarity"):
  with members:
    - "a large annotated corpus for learning natural language inference"
    - "adam a method for stochastic optimization"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "improving zero shot learning by mitigating the hubness problem"
    - "distributed representations of words and phrases and their compositionality"
    - "exploiting similarities among languages for machine translation"
    - "the jrc acquis a multilingual aligned parallel corpus with 20 languages"

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)