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 "neural embedding propagation on heterogeneous networks"):
  Abstract: Classification is one of the most important problems in machine learning. To address label scarcity, semi-supervised learning (SSL) has been intensively studied over the past two decades, which mainly leverages data affinity modeled by networks. Label propagation (LP), however, as the most popular SSL technique, mostly only works on homogeneous networks with single-typed simple interactions. In this work, we focus on the more general and powerful heterogeneous networks, which accommodate multi-typed objects and links, and thus endure multi-typed complex interactions. Specifically, we propose \textit{neural embedding propagation} (NEP), which leverages distributed embeddings to represent objects and dynamically composed modular networks to model their complex interactions. While generalizing LP as a simple instance, NEP is far more powerful in its natural awareness of different types of objects and links, and the ability to automatically capture their important interaction patterns. Further, we develop a series of efficient training strategies for NEP, leading to its easy deployment on real-world heterogeneous networks with millions of objects. With extensive experiments on three datasets, we comprehensively demonstrate the effectiveness, efficiency, and robustness of NEP compared with state-of-the-art network embedding and SSL algorithms.

Includes members:
  Member "similarity modeling on heterogeneous networks via automatic path discovery":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cube2net efficient query specific network construction with data cube organization")
  Member "meta graph based hin spectral embedding methods analyses and insights":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cube2net efficient query specific network construction with data cube organization")
  Member "user guided clustering in heterogeneous information networks via motif based comprehensive transcription":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cube2net efficient query specific network construction with data cube organization")
  Member "prep path based relevance from a probabilistic perspective in heterogeneous information networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "similarity modeling on heterogeneous networks via automatic path discovery")
  Member "model agnostic meta learning for fast adaptation of deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "meta path guided embedding for similarity search in large scale heterogeneous information networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "magnn metapath aggregated graph neural network for heterogeneous graph embedding")
  Member "higher order organization of complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hypergraph motifs concepts algorithms and discoveries")
  Member "task guided and path augmented heterogeneous network embedding for author identification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "attentive knowledge graph embedding for personalized recommendation")

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)