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: "poincar e embeddings for learning hierarchical representations"
  Abstract: Representation learning has become an invaluable approach for learning from symbolic data such as text and graphs. However, while complex symbolic datasets often exhibit a latent hierarchical structure, state-of-the-art methods typically learn embeddings in Euclidean vector spaces, which do not account for this property. For this purpose, we introduce a new approach for learning hierarchical representations of symbolic data by embedding them into hyperbolic space -- or more precisely into an n-dimensional Poincare ball. Due to the underlying hyperbolic geometry, this allows us to learn parsimonious representations of symbolic data by simultaneously capturing hierarchy and similarity. We introduce an efficient algorithm to learn the embeddings based on Riemannian optimization and show experimentally that Poincare embeddings outperform Euclidean embeddings significantly on data with latent hierarchies, both in terms of representation capacity and in terms of generalization ability.

Connected through hyperedge HE-1 (induced by source paper "the tensor brain semantic decoding for perception and memory"):
  with members:
    - "reasoning on knowledge graphs with debate dynamics"
    - "improving visual relation detection using depth maps"
    - "learning by abstraction the neural state machine"
    - "context aware zero shot recognition"
    - "embedding models for episodic knowledge graphs"
    - "improving information extraction from images with learned semantic models"
    - "graph r cnn for scene graph generation"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "an end to end approach for the verification problem learning the right distance"):
  with members:
    - "an inductive bias for distances neural nets that respect the triangle inequality"
    - "contrastive multiview coding"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "voxceleb2 deep speaker recognition"
    - "additive margin softmax for face verification"
    - "learning non metric visual similarity for image retrieval"
    - "representation learning with contrastive predictive coding"
    - "sampling matters in deep embedding learning"

Connected through hyperedge HE-3 (induced by source paper "latent variable modelling with hyperbolic normalizing flows"):
  with members:
    - "adversarial autoencoders with constant curvature latent manifolds"
    - "riemannian normalizing flow on variational wasserstein autoencoder for text modeling"
    - "graph normalizing flows"
    - "mixed curvature variational autoencoders"
    - "poincare wasserstein autoencoder"
    - "hyperbolic graph neural networks"
    - "hyperbolic image embeddings"
    - "hyperbolic graph convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "product knowledge graph embedding for e commerce"):
  with members:
    - "analogies explained towards understanding word embeddings"
    - "a multi task learning approach for improving product title compression with user search log data"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "multi task learning as multi objective optimization"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "attention is all you need"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks"

Connected through hyperedge HE-5 (induced by source paper "binarized pmi matrix bridging word embeddings and hyperbolic spaces"):
  with members:
    - "analogies explained towards understanding word embeddings"
    - "visualizing and measuring the geometry of bert"
    - "low rank approximation of matrices for pmi based word embeddings"
    - "poincar e glove hyperbolic word embeddings"
    - "towards understanding linear word analogies"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "what the vec towards probabilistically grounded embeddings"

Connected through hyperedge HE-6 (induced by source paper "computationally tractable riemannian manifolds for graph embeddings"):
  with members:
    - "riemannian adaptive optimization methods"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "grassmannian learning embedding geometry awareness in shallow and deep learning"
    - "hyperbolic entailment cones for learning hierarchical embeddings"
    - "node2vec scalable feature learning for networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "complex embeddings for simple link prediction"
    - "deep convolutional networks on graph structured data"

Connected through hyperedge HE-7 (induced by source paper "social centralization and semantic collapse hyperbolic embeddings of networks and text"):
  with members:
    - "the wisdom of polarized crowds"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "word embeddings quantify 100 years of gender and ethnic stereotypes"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "network mapping by replaying hyperbolic growth"
    - "deepwalk online learning of social representations"
    - "distributed representations of words and phrases and their compositionality"
    - "echoes of power language effects and power differences in social interaction"

Connected through hyperedge HE-8 (induced by source paper "representation learning for electronic health records"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "multimodal multitask representation learning for pathology biobank metadata prediction"
    - "enhancing clinical concept extraction with contextual embeddings"
    - "clinicalbert modeling clinical notes and predicting hospital readmission"
    - "clinical concept extraction for document level coding"
    - "graph convolutional transformer learning the graphical structure of electronic health records"
    - "unsupervised clinical language translation"
    - "publicly available clinical bert embeddings"

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.AI(Artificial Intelligence)