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: "learning semantic similarity for very short texts"
  Abstract: Levering data on social media, such as Twitter and Facebook, requires information retrieval algorithms to become able to relate very short text fragments to each other. Traditional text similarity methods such as tf-idf cosine-similarity, based on word overlap, mostly fail to produce good results in this case, since word overlap is little or non-existent. Recently, distributed word representations, or word embeddings, have been shown to successfully allow words to match on the semantic level. In order to pair short text fragments -- as a concatenation of separate words -- an adequate distributed sentence representation is needed, in existing literature often obtained by naively combining the individual word representations. We therefore investigated several text representations as a combination of word embeddings in the context of semantic pair matching. This paper investigates the effectiveness of several such naive techniques, as well as traditional tf-idf similarity, for fragments of different lengths. Our main contribution is a first step towards a hybrid method that combines the strength of dense distributed representations -- as opposed to sparse term matching -- with the strength of tf-idf based methods to automatically reduce the impact of less informative terms. Our new approach outperforms the existing techniques in a toy experimental set-up, leading to the conclusion that the combination of word embeddings and tf-idf information might lead to a better model for semantic content within very short text fragments.

Connected through hyperedge HE-1 (induced by source paper "correlations between word vector sets"):
  with members:
    - "correlation coefficients and semantic textual similarity"
    - "similarity of neural network representations revisited"
    - "model comparison for semantic grouping"
    - "don t settle for average go for the max fuzzy sets and max pooled word vectors"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "a rank based similarity metric for word embeddings"

Connected through hyperedge HE-2 (induced by source paper "learning semantic sentence representations from visually grounded language without lexical knowledge"):
  with members:
    - "learning semantic textual similarity from conversations"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "using sparse semantic embeddings learned from multimodal text and image data to model human conceptual knowledge"
    - "predicting visual features from text for image and video caption retrieval"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "order embeddings and character level convolutions for multimodal alignment"
    - "learning visually grounded sentence representations"
    - "lstm a search space odyssey"

Connected through hyperedge HE-3 (induced by source paper "correlation coefficients and semantic textual similarity"):
  with members:
    - "don t settle for average go for the max fuzzy sets and max pooled word vectors"
    - "deep contextualized word representations"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "a rank based similarity metric for word embeddings"
    - "decoding decoders finding optimal representation spaces for unsupervised similarity tasks"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "senteval an evaluation toolkit for universal sentence representations"

Connected through hyperedge HE-4 (induced by source paper "a multi resolution word embedding for document retrieval from large unstructured knowledge bases"):
  with members:
    - "deep contextualized word representations"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "text embeddings for retrieval from a large knowledge base"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "quasar datasets for question answering by search and reading"
    - "advances in pre training distributed word representations"
    - "deep metric learning with angular loss"

Connected through hyperedge HE-5 (induced by source paper "analyzing and learning the language for different types of harassment"):
  with members:
    - "large scale crowdsourcing and characterization of twitter abusive behavior"
    - "abusniff automatic detection and defenses against abusive facebook friends"
    - "hate lingo a target based linguistic analysis of hate speech in social media"
    - "a quality type aware annotated corpus and lexicon for harassment research"
    - "mean birds detecting aggression and bullying on twitter"
    - "automated hate speech detection and the problem of offensive language"
    - "advances in pre training distributed word representations"
    - "automatic detection of fake news"

Connected through hyperedge HE-6 (induced by source paper "representation learning for very short texts using weighted word embedding aggregation"):
  with members:
    - "a hierarchical recurrent encoder decoder for generative context aware query suggestion"
    - "skip thought vectors"
    - "convolutional neural network architectures for matching natural language sentences"
    - "distributed representations of sentences and documents"
    - "n gram based low dimensional representation for document classification"
    - "sequence to sequence learning with neural networks"
    - "a convolutional neural network for modelling sentences"
    - "efficient estimation of word representations in vector space"

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.IR(Information Retrieval)