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: "skip gram word embeddings in hyperbolic space"
  Abstract: Embeddings of tree-like graphs in hyperbolic space were recently shown to surpass their Euclidean counterparts in performance by a large margin. Inspired by these results, we present an algorithm for learning word embeddings in hyperbolic space from free text. An objective function based on the hyperbolic distance is derived and included in the skip-gram architecture from word2vec. The hyperbolic word embeddings are then evaluated on word similarity and analogy benchmarks. The results demonstrate the potential of hyperbolic word embeddings, particularly in low dimensions, though without clear superiority over their Euclidean counterparts. We further discuss problems in the formulation of the analogy task resulting from the curvature of hyperbolic space.

Connected through hyperedge HE-1 (induced by source paper "fine grained entity typing in hyperbolic space"):
  with members:
    - "imposing label relational inductive bias for extremely fine grained entity typing"
    - "hyperbolic attention networks"
    - "hyperbolic representation learning for fast and efficient neural question answering"
    - "ultra fine entity typing"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "embedding text in hyperbolic spaces"
    - "representation tradeoffs for hyperbolic embeddings"
    - "hierarchical losses and new resources for fine grained entity typing and linking"

Connected through hyperedge HE-2 (induced by source paper "hyperbolic interaction model for hierarchical multi label classification"):
  with members:
    - "hyperbolic attention networks"
    - "combined convolutional and recurrent neural networks for hierarchical classification of images"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "hyperbolic neural networks"
    - "embedding text in hyperbolic spaces"
    - "joint embedding of words and labels for text classification"
    - "representation tradeoffs for hyperbolic embeddings"
    - "weakly supervised hierarchical text classification"

Connected through hyperedge HE-3 (induced by source paper "scalable hyperbolic recommender systems"):
  with members:
    - "hyperbolic attention networks"
    - "hyperbolic recommender systems"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "poincar e glove hyperbolic word embeddings"
    - "hyperbolic neural networks"
    - "embedding text in hyperbolic spaces"
    - "representation tradeoffs for hyperbolic embeddings"
    - "hyperbolic entailment cones for learning hierarchical embeddings"

Connected through hyperedge HE-4 (induced by source paper "representation learning with weighted inner product for universal approximation of general similarities"):
  with members:
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "distilled wasserstein learning for word embedding and topic modeling"
    - "poincar e embeddings for learning hierarchical representations"
    - "neural networks and rational functions"
    - "error bounds for approximations with deep relu networks"
    - "sliced wasserstein kernels for probability distributions"
    - "a primer on neural network models for natural language processing"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-5 (induced by source paper "poincar e glove hyperbolic word embeddings"):
  with members:
    - "riemannian adaptive optimization methods"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "context mover s distance barycenters optimal transport of contexts for building representations"
    - "hyperbolic neural networks"
    - "embedding text in hyperbolic spaces"
    - "hierarchical density order embeddings"
    - "representation tradeoffs for hyperbolic embeddings"
    - "hyperbolic entailment cones for learning hierarchical embeddings"

Connected through hyperedge HE-6 (induced by source paper "hyperbolic deep learning for chinese natural language understanding"):
  with members:
    - "hyperbolic attention networks"
    - "riemannian adaptive optimization methods"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "poincar e glove hyperbolic word embeddings"
    - "hyperbolic neural networks"
    - "state of the art chinese word segmentation with bi lstms"
    - "attention is all you need"
    - "sgdr stochastic gradient descent with restarts"

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)