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 "representation learning with weighted inner product for universal approximation of general similarities"):
  Abstract: We propose $\textit{weighted inner product similarity}$ (WIPS) for neural-network based graph embedding, where we optimize the weights of the inner product in addition to the parameters of neural networks. Despite its simplicity, WIPS can approximate arbitrary general similarities including positive definite, conditionally positive definite, and indefinite kernels. WIPS is free from similarity model selection, yet it can learn any similarity models such as cosine similarity, negative Poincare distance and negative Wasserstein distance. Our extensive experiments show that the proposed method can learn high-quality distributed representations of nodes from real datasets, leading to an accurate approximation of similarities as well as high performance in inductive tasks.

Includes members:
  Member "skip gram word embeddings in hyperbolic space":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fine grained entity typing in hyperbolic space")
  Member "learning continuous hierarchies in the lorentz model of hyperbolic geometry":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discriminative topic mining via category name guided text embedding")
  Member "distilled wasserstein learning for word embedding and topic modeling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discriminative topic mining via category name guided text embedding")
  Member "poincar e embeddings for learning hierarchical representations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the tensor brain semantic decoding for perception and memory")
  Member "neural networks and rational functions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "pad e activation units end to end learning of flexible activation functions in deep networks")
  Member "error bounds for approximations with deep relu networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "neural contextual bandits with ucb based exploration")
  Member "sliced wasserstein kernels for probability distributions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning autoencoders with relational regularization")
  Member "a primer on neural network models for natural language processing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "theory in theory out how social theory can solve problems that machine learning can t")

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)