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: "hyperbolic recommender systems"
  Abstract: Many well-established recommender systems are based on representation learning in Euclidean space. In these models, matching functions such as the Euclidean distance or inner product are typically used for computing similarity scores between user and item embeddings. This paper investigates the notion of learning user and item representations in Hyperbolic space. In this paper, we argue that Hyperbolic space is more suitable for learning user-item embeddings in the recommendation domain. Unlike Euclidean spaces, Hyperbolic spaces are intrinsically equipped to handle hierarchical structure, encouraged by its property of exponentially increasing distances away from origin. We propose HyperBPR (Hyperbolic Bayesian Personalized Ranking), a conceptually simple but highly effective model for the task at hand. Our proposed HyperBPR not only outperforms their Euclidean counterparts, but also achieves state-of-the-art performance on multiple benchmark datasets, demonstrating the effectiveness of personalized recommendation in Hyperbolic space.

Connected through hyperedge HE-1 (induced by source paper "learning feature interactions with lorentzian factorization machine"):
  with members:
    - "scalable hyperbolic recommender systems"
    - "autoint automatic feature interaction learning via self attentive neural networks"
    - "hyperbolic attention networks"
    - "riemannian adaptive optimization methods"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "representation tradeoffs for hyperbolic embeddings"
    - "hyperbolic entailment cones for learning hierarchical embeddings"

Connected through hyperedge HE-2 (induced by source paper "beyond vector spaces compact data representationas differentiable weighted graphs"):
  with members:
    - "robust graph learning from noisy data"
    - "dag gnn dag structure learning with graph neural networks"
    - "hyperbolic image embeddings"
    - "a comprehensive survey on graph neural networks"
    - "poincar e glove hyperbolic word embeddings"
    - "voxceleb2 deep speaker recognition"
    - "representation tradeoffs for hyperbolic embeddings"
    - "neural collaborative filtering"

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

Connected through hyperedge HE-4 (induced by source paper "scalable hyperbolic recommender systems"):
  with members:
    - "hyperbolic attention networks"
    - "skip gram word embeddings in hyperbolic space"
    - "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-5 (induced by source paper "hebbian graph embeddings"):
  with members:
    - "scalable hyperbolic recommender systems"
    - "link prediction based on graph neural networks"
    - "graph embedding techniques applications and performance a survey"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "node2vec scalable feature learning for networks"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-6 (induced by source paper "music recommendations in hyperbolic space an application of empirical bayes and hierarchical poincare embeddings"):
  with members:
    - "poincar e embeddings for learning hierarchical representations"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-7 (induced by source paper "metric learning on manifolds"):
  with members:
    - "poincar e embeddings for learning hierarchical representations"
    - "sample complexity of learning mahalanobis distance metrics"

Connected through hyperedge HE-8 (induced by source paper "riemannian adaptive optimization methods"):
  with members:
    - "on the convergence of adam and beyond"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "hyperbolic neural networks"
    - "averaging stochastic gradient descent on riemannian manifolds"
    - "representation tradeoffs for hyperbolic embeddings"
    - "hyperbolic entailment cones for learning hierarchical embeddings"
    - "riemannian approach to batch normalization"
    - "riemannian dictionary learning and sparse coding for positive definite matrices"

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.IR(Information Retrieval)