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: "analytical methods for interpretable ultradense word embeddings"
  Abstract: Word embeddings are useful for a wide variety of tasks, but they lack interpretability. By rotating word spaces, interpretable dimensions can be identified while preserving the information contained in the embeddings without any loss. In this work, we investigate three methods for making word spaces interpretable by rotation: Densifier (Rothe et al., 2016), linear SVMs and DensRay, a new method we propose. While DensRay is very closely related to the Densifier, it can be computed in closed form, is hyperparameter-free and thus more robust than the Densifier. We evaluate the methods on lexicon induction and set-based word analogy and conclude that analytical methods such as DensRay and SVMs are preferable. For word analogy we propose a new method to solve the task which outperforms the previous state of the art by large margins.

Connected through hyperedge HE-1 (induced by source paper "interpretable word embeddings via informative priors"):
  with members:
    - "identification interpretability and bayesian word embeddings"
    - "lipstick on a pig debiasing methods cover up systematic gender biases in word embeddings but do not remove them"
    - "the geometry of culture analyzing the meanings of class through word embeddings"
    - "word embeddings quantify 100 years of gender and ethnic stereotypes"
    - "learning gender neutral word embeddings"
    - "bayesian neural word embedding"
    - "sparse overcomplete word vector representations"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "imparting interpretability to word embeddings while preserving semantic structure"):
  with members:
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "semantic structure and interpretability of word embeddings"
    - "adversarial propagation and zero shot cross lingual transfer of word vector specialization"
    - "rotations and interpretability of word embeddings the case of the russian language"
    - "semantic specialisation of distributional word vector spaces using monolingual and cross lingual constraints"
    - "spine sparse interpretable neural embeddings"
    - "jointly learning word embeddings and latent topics"
    - "mixing dirichlet topic models and word embeddings to make lda2vec"

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)