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 "relation learning on social networks with multi modal graph edge variational autoencoders"):
  Abstract: While node semantics have been extensively explored in social networks, little research attention has been paid to profile edge semantics, i.e., social relations. Ideal edge semantics should not only show that two users are connected, but also why they know each other and what they share in common. However, relations in social networks are often hard to profile, due to noisy multi-modal signals and limited user-generated ground-truth labels. #R##N#In this work, we aim to develop a unified and principled framework that can profile user relations as edge semantics in social networks by integrating multi-modal signals in the presence of noisy and incomplete data. Our framework is also flexible towards limited or missing supervision. Specifically, we assume a latent distribution of multiple relations underlying each user link, and learn them with multi-modal graph edge variational autoencoders. We encode the network data with a graph convolutional network, and decode arbitrary signals with multiple reconstruction networks. Extensive experiments and case studies on two public DBLP author networks and two internal LinkedIn member networks demonstrate the superior effectiveness and efficiency of our proposed model.

Includes members:
  Member "i know you ll be back interpretable new user clustering and churn prediction on a mobile social application":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "model agnostic linear competitors when interpretable models compete and collaborate with black box models")
  Member "similarity modeling on heterogeneous networks via automatic path discovery":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cube2net efficient query specific network construction with data cube organization")
  Member "neural embedding propagation on heterogeneous networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cube2net efficient query specific network construction with data cube organization")
  Member "cubenet multi facet hierarchical heterogeneous network construction analysis and mining":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cube2net efficient query specific network construction with data cube organization")
  Member "representation learning on graphs with jumping knowledge networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep graph matching consensus")
  Member "fastgcn fast learning with graph convolutional networks via importance sampling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "spectral graph attention network")
  Member "relationship profiling over social networks reverse smoothness from similarity to closeness":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cube2net efficient query specific network construction with data cube organization")
  Member "metapad meta pattern discovery from massive text corpora":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "taxoexpan self supervised taxonomy expansion with position enhanced graph neural network")

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)