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: "embedding semantic relations into word representations"
  Abstract: Learning representations for semantic relations is important for various tasks such as analogy detection, relational search, and relation classification. Although there have been several proposals for learning representations for individual words, learning word representations that explicitly capture the semantic relations between words remains under developed. We propose an unsupervised method for learning vector representations for words such that the learnt representations are sensitive to the semantic relations that exist between two words. First, we extract lexical patterns from the co-occurrence contexts of two words in a corpus to represent the semantic relations that exist between those two words. Second, we represent a lexical pattern as the weighted sum of the representations of the words that co-occur with that lexical pattern. Third, we train a binary classifier to detect relationally similar vs. non-similar lexical pattern pairs. The proposed method is unsupervised in the sense that the lexical pattern pairs we use as train data are automatically sampled from a corpus, without requiring any manual intervention. Our proposed method statistically significantly outperforms the current state-of-the-art word representations on three benchmark datasets for proportional analogy detection, demonstrating its ability to accurately capture the semantic relations among words.

Connected through hyperedge HE-1 (induced by source paper "neural latent relational analysis to capture lexical semantic relations in a vector space"):
  with members:
    - "filling missing paths modeling co occurrences of word pairs and dependency paths for recognizing lexical semantic relations"
    - "analogical inference for multi relational embeddings"
    - "generalizing to unseen entities and entity pairs with row less universal schema"
    - "improving hypernymy detection with an integrated path based and distributional method"
    - "complex embeddings for simple link prediction"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "holographic embeddings of knowledge graphs"
    - "embedding entities and relations for learning and inference in knowledge bases"

Connected through hyperedge HE-2 (induced by source paper "an interpretable knowledge transfer model for knowledge base completion"):
  with members:
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"
    - "neighborhood mixture model for knowledge base completion"
    - "lightrnn memory and computation efficient recurrent neural networks"
    - "link prediction using embedded knowledge graphs"
    - "stranse a novel embedding model of entities and relationships in knowledge bases"
    - "transfer learning for low resource neural machine translation"
    - "sparse overcomplete word vector representations"

Connected through hyperedge HE-3 (induced by source paper "information extraction in illicit web domains"):
  with members:
    - "a non parametric learning approach to identify online human trafficking"
    - "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)