human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "learning graph structured data using poincar e embeddings and riemannian k means algorithms"):
  Abstract: Recent literature has shown several benefits of hyperbolic embedding of graph-structured data (GSD) in representing their structures and latent relations. While several studies have explored the ability of hyperbolic embedding to represent data (for example, by quantifying their mean average precision) and their ability to produce better visualisations of clusters, only few works exploited the effectiveness of hyperbolic embedding to perform learning on the initial GSD. #R##N#Motivated by innovative ideas from the fields of Brain computer interfaces and Radar processing, this paper presents a new scheme for learning GSD based on hyperbolic embedding, Riemannian barycentre (i.e. Frechet or geometric mean) and $K$-means algorithms as a significant tool that derives from it. The main idea is as follows. Relying on the Riemannian barycentre, we define a notion of minimal variance which allows us to choose an embedding between different ones. This embedding is used thereafter together with $K$-means algorithms to perform unsupervised clustering and in combination with the nearest neighbour rule to perform supervised learning. We demonstrate the performance of the proposed framework through several experiments on real-world social networks and hierarchical GSD. The obtained results outperform their counterparts in high-dimensional Euclidean spaces and recent proposed geometric approaches.

Includes members:
  Member "metric learning on manifolds":
    also connected through other hyperedges:
      (none)
  Member "a survey on network embedding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to segment 3d point clouds in 2d image space")
  Member "graph2vec learning distributed representations of graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "few shot learning on graphs via super classes based on graph spectral measures")
  Member "community enhanced network representation learning for network analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "from node embedding to community embedding a hyperbolic approach")
  Member "from node embedding to community embedding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ddgk learning graph representations for deep divergence graph kernels")
  Member "deepwalk online learning of social representations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "memory based graph networks")
  Member "distributed representations of words and phrases and their compositionality":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "sustaining the internet with hyperbolic mapping":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hyperkg hyperbolic knowledge graph embeddings for knowledge base completion")

Question: Please tell me which class the source paper 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)