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 "meta graph based hin spectral embedding methods analyses and insights"):
  Abstract: In this work, we propose to study the utility of different meta-graphs, as well as how to simultaneously leverage multiple meta-graphs for HIN embedding in an unsupervised manner. Motivated by prolific research on homogeneous networks, especially spectral graph theory, we firstly conduct a systematic empirical study on the spectrum and embedding quality of different meta-graphs on multiple HINs, which leads to an efficient method of meta-graph assessment. It also helps us to gain valuable insight into the higher-order organization of HINs and indicates a practical way of selecting useful embedding dimensions. Further, we explore the challenges of combining multiple meta-graphs to capture the multi-dimensional semantics in HIN through reasoning from mathematical geometry and arrive at an embedding compression method of autoencoder with $\ell_{2,1}$-loss, which finds the most informative meta-graphs and embeddings in an end-to-end unsupervised manner. Finally, empirical analysis suggests a unified workflow to close the gap between our meta-graph assessment and combination methods. To the best of our knowledge, this is the first research effort to provide rich theoretical and empirical analyses on the utility of meta-graphs and their combinations, especially regarding HIN embedding. Extensive experimental comparisons with various state-of-the-art neural network based embedding methods on multiple real-world HINs demonstrate the effectiveness and efficiency of our framework in finding useful meta-graphs and generating high-quality HIN embeddings.

Includes members:
  Member "network embedding as matrix factorization unifying deepwalk line pte and node2vec":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial attacks and defenses on graphs a review and empirical study")
  Member "submodular hypergraphs p laplacians cheeger inequalities and spectral clustering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "parameterized objectives and algorithms for clustering bipartite graphs and hypergraphs")
  Member "aspem embedding learning by aspects in heterogeneous information networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "task guided pair embedding in heterogeneous network")
  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 "neural embedding propagation on heterogeneous networks")
  Member "heterogeneous information network embedding for meta path based proximity":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep representation learning for social network analysis")
  Member "multi view graph embedding with hub detection for brain network analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep graph similarity learning a survey")
  Member "meta path guided embedding for similarity search in large scale heterogeneous information networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (induced by source paper "hypergraph motifs concepts algorithms and discoveries")

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)