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 "network representation learning for link prediction are we improving upon simple heuristics"):
  Abstract: Network representation learning has become an active research area in recent years with many new methods showcasing their performance on downstream prediction tasks such as Link Prediction. Despite the efforts of the community to ensure reproducibility of research by providing method implementations, important issues remain. The complexity of the evaluation pipelines and abundance of design choices have led to difficulties in quantifying the progress in the field and identifying the state-of-the-art. In this work, we analyse 17 network embedding methods on 7 real-world datasets and find, using a consistent evaluation pipeline, only thin progress over the recent years. Also, many embedding methods are outperformed by simple heuristics. Finally, we discuss how standardized evaluation tools can repair this situation and boost progress in this field.

Includes members:
  Member "network representation learning consolidation and renewed bearing":
    also connected through other hyperedges:
      (none)
  Member "evalne a framework for evaluating network embeddings on link prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "scalable dyadic independence models with local and global constraints")
  Member "verse versatile graph embeddings from similarity measures":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "graph ordering towards the optimal by learning")
  Member "graph embedding techniques applications and performance a survey":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "struc2vec learning node representations from structural identity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cube2net efficient query specific network construction with data cube organization")
  Member "watch your step learning node embeddings via graph attention":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "uwb gcn accelerating graph convolutional networks through runtime workload rebalancing")
  Member "representation learning on graphs methods and applications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning to segment 3d point clouds in 2d image space")
  Member "analysis of the impact of negative sampling on link prediction in knowledge graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "probability calibration for knowledge graph embedding models")

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)