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 "cube2net efficient query specific network construction with data cube organization"):
  Abstract: Networks are widely used to model objects with interactions and have enabled various downstream applications. However, in the real world, network mining is often done on particular query sets of objects, which does not require the construction and computation of networks including all objects in the datasets. In this work, for the first time, we propose to address the problem of query-specific network construction, to break the efficiency bottlenecks of existing network mining algorithms and facilitate various downstream tasks. To deal with real-world massive networks with complex attributes, we propose to leverage the well-developed data cube technology to organize network objects w.r.t. their essential attributes. An efficient reinforcement learning algorithm is then developed to automatically explore the data cube structures and construct the optimal query-specific networks. With extensive experiments of two classic network mining tasks on different real-world large datasets, we show that our proposed cube2net pipeline is general, and much more effective and efficient in query-specific network construction, compared with other methods without the leverage of data cube or reinforcement learning.

Includes members:
  Member "similarity modeling on heterogeneous networks via automatic path discovery":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "relation learning on social networks with multi modal graph edge variational autoencoders")
  Member "neural embedding propagation on heterogeneous networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "relation learning on social networks with multi modal graph edge variational autoencoders")
  Member "relation learning on social networks with multi modal graph edge variational autoencoders":
    also connected through other hyperedges:
      (none)
  Member "meta graph based hin spectral embedding methods analyses and insights":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discovering hypernymy in text rich heterogeneous information network by exploiting context granularity")
  Member "place deduplication with embeddings":
    also connected through other hyperedges:
      (none)
  Member "query specific knowledge summarization with entity evolutionary networks":
    also connected through other hyperedges:
      (none)
  Member "cubenet multi facet hierarchical heterogeneous network construction analysis and mining":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "relation learning on social networks with multi modal graph edge variational autoencoders")
  Member "user guided clustering in heterogeneous information networks via motif based comprehensive transcription":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "neural embedding propagation on heterogeneous networks")

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.SI(Social and Information Networks)