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 "a semantic rich similarity measure in heterogeneous information networks"):
  Abstract: Measuring the similarities between objects in information networks has fundamental importance in recommendation systems, clustering and web search. The existing metrics depend on the meta path or meta structure specified by users. In this paper, we propose a stratified meta structure based similarity $SMSS$ in heterogeneous information networks. The stratified meta structure can be constructed automatically and capture rich semantics. Then, we define the commuting matrix of the stratified meta structure by virtue of the commuting matrices of meta paths and meta structures. As a result, $SMSS$ is defined by virtue of these commuting matrices. Experimental evaluations show that the proposed $SMSS$ on the whole outperforms the state-of-the-art metrics in terms of ranking and clustering.

Includes members:
  Member "forward backward similarity search in knowledge networks":
    also connected through other hyperedges:
      (none)
  Member "a survey of heterogeneous information network analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "prince provider side interpretability with counterfactual explanations in recommender systems")
  Member "hetesim a general framework for relevance measure in heterogeneous networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep collaborative filtering with multi aspect information in heterogeneous networks")
  Member "relation strength aware clustering of heterogeneous information networks with incomplete attributes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hahe hierarchical attentive heterogeneous information network embedding")

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.DB(Databases)