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: "spherical text embedding"
  Abstract: Unsupervised text embedding has shown great power in a wide range of NLP tasks. While text embeddings are typically learned in the Euclidean space, directional similarity is often more effective in tasks such as word similarity and document clustering, which creates a gap between the training stage and usage stage of text embedding. To close this gap, we propose a spherical generative model based on which unsupervised word and paragraph embeddings are jointly learned. To learn text embeddings in the spherical space, we develop an efficient optimization algorithm with convergence guarantee based on Riemannian optimization. Our model enjoys high efficiency and achieves state-of-the-art performances on various text embedding tasks including word similarity and document clustering.

Connected through hyperedge HE-1 (induced by source paper "discriminative topic mining via category name guided text embedding"):
  with members:
    - "guiding corpus based set expansion by auxiliary sets generation and co expansion"
    - "higitclass keyword driven hierarchical classification of github repositories"
    - "improving topic models with latent feature word representations"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "distilled wasserstein learning for word embedding and topic modeling"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "joint embedding of words and labels for text classification"
    - "weakly supervised hierarchical text classification"

Connected through hyperedge HE-2 (induced by source paper "separate and attend in personal email search"):
  with members:
    - "tf ranking scalable tensorflow library for learning to rank"
    - "area attention"
    - "domain adaptation for enterprise email search"
    - "multi task learning for email search ranking with auxiliary query clustering"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deeprank a new deep architecture for relevance ranking in information retrieval"
    - "attention is all you need"
    - "neural ranking models with weak supervision"

Connected through hyperedge HE-3 (induced by source paper "grand challenges of resilience autonomous system resilience through design and runtime measures"):
  with members:
    - "straggler mitigation with tiered gradient codes"
    - "certified adversarial robustness via randomized smoothing"
    - "lower bounds on adversarial robustness from optimal transport"
    - "a reinforcement learning based approach for joint multi agent decision making"
    - "vstore a data store for analytics on large videos"
    - "optimal server selection for straggler mitigation"
    - "formal security analysis of neural networks using symbolic intervals"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "cate category name guidedword embedding"):
  with members:
    - "guiding corpus based set expansion by auxiliary sets generation and co expansion"
    - "topic modeling in embedding spaces"
    - "higitclass keyword driven hierarchical classification of github repositories"
    - "improving topic models with latent feature word representations"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "poincar e glove hyperbolic word embeddings"
    - "distilled wasserstein learning for word embedding and topic modeling"
    - "deep contextualized word representations"

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)