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: "improving textual network learning with variational homophilic embeddings"
  Abstract: The performance of many network learning applications crucially hinges on the success of network embedding algorithms, which aim to encode rich network information into low-dimensional vertex-based vector representations. This paper considers a novel variational formulation of network embeddings, with special focus on textual networks. Different from most existing methods that optimize a discriminative objective, we introduce Variational Homophilic Embedding (VHE), a fully generative model that learns network embeddings by modeling the semantic (textual) information with a variational autoencoder, while accounting for the structural (topology) information through a novel homophilic prior design. Homophilic vertex embeddings encourage similar embedding vectors for related (connected) vertices. The proposed VHE promises better generalization for downstream tasks, robustness to incomplete observations, and the ability to generalize to unseen vertices. Extensive experiments on real-world networks, for multiple tasks, demonstrate that the proposed method consistently achieves superior performance relative to competing state-of-the-art approaches.

Connected through hyperedge HE-1 (induced by source paper "an optimal transport framework for zero shot learning"):
  with members:
    - "improving textual network embedding with global attention via optimal transport"
    - "topic guided variational autoencoders for text generation"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "discriminative learning of latent features for zero shot recognition"
    - "nash toward end to end neural architecture for generative semantic hashing"
    - "distilled wasserstein learning for word embedding and topic modeling"
    - "zero shot kernel learning"
    - "improving gans using optimal transport"

Connected through hyperedge HE-2 (induced by source paper "graph driven generative models for heterogeneous multi task learning"):
  with members:
    - "medgcn graph convolutional networks for multiple medical tasks"
    - "explainable prediction of medical codes from clinical text"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "distilled wasserstein learning for word embedding and topic modeling"
    - "graph convolutional networks for text classification"
    - "joint embedding of words and labels for text classification"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "an empirical evaluation of deep learning for icd 9 code assignment using mimic iii clinical notes"

Connected through hyperedge HE-3 (induced by source paper "collaborative filtering with a synthetic feedback loop"):
  with members:
    - "topic guided variational autoencoders for text generation"
    - "neurec on nonlinear transformation for personalized ranking"
    - "recommendations with negative feedback via pairwise deep reinforcement learning"
    - "wasserstein autoencoders for collaborative filtering"
    - "outer product based neural collaborative filtering"
    - "top k off policy correction for a reinforce recommender system"
    - "zero shot learning via class conditioned deep generative models"
    - "neural collaborative filtering"

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.LG(Machine Learning)