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 "hyperbolic node embedding for signed networks"):
  Abstract: The rapid evolving World Wide Web has produced a large amount of complex and heterogeneous network data. To facilitate network analysis algorithms, signed network embedding methods automatically learn feature vectors of nodes in signed networks. However, existing algorithms only managed to embed the networks into Euclidean spaces, although many features of signed networks reported are more suitable for non-Euclidean space. Besides, previous works also do not consider the hierarchical organization of networks, which is widely existed in real-world networks. In this work, we investigate the problem of whether the hyperbolic space is a better choice to represent signed networks. We develop a non-Euclidean signed network embedding method based on structural balance theory and Riemannian optimization. Our method embeds signed networks into a Poincare ball, which is a hyperbolic space can be seen as a continuous tree. This feature enables our approach to capture underlying hierarchical structure in signed networks. We empirically compare our method with three Euclidean-based baselines in visualization, sign prediction, and reconstruction tasks on six real-world datasets. The results show that our hyperbolic embedding performs better than Euclidean counterparts and can extract a meaningful latent hierarchical structure from signed networks.

Includes members:
  Member "poincar e glove hyperbolic word embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "binarized pmi matrix bridging word embeddings and hyperbolic spaces")
  Member "graph embedding techniques applications and performance a survey":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "hyperbolic entailment cones for learning hierarchical embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discriminative topic mining via category name guided text embedding")
  Member "sne signed network embedding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "scalable graph embeddings via sparse transpose proximities")
  Member "node2vec scalable feature learning for networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep graph matching consensus")
  Member "a survey of signed network mining in social media":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "social science guided feature engineering a novel approach to signed link analysis")
  Member "deepwalk online learning of social representations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "memory based graph networks")
  Member "exploiting social network structure for person to person sentiment analysis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "user profiling using hinge loss markov random fields")

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)