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 "linknbed multi graph representation learning with entity linkage"):
  Abstract: Knowledge graphs have emerged as an important model for studying complex multi-relational data. This has given rise to the construction of numerous large scale but incomplete knowledge graphs encoding information extracted from various resources. An effective and scalable approach to jointly learn over multiple graphs and eventually construct a unified graph is a crucial next step for the success of knowledge-based inference for many downstream applications. To this end, we propose LinkNBed, a deep relational learning framework that learns entity and relationship representations across multiple graphs. We identify entity linkage across graphs as a vital component to achieve our goal. We design a novel objective that leverage entity linkage and build an efficient multi-task training procedure. Experiments on link prediction and entity linkage demonstrate substantial improvements over the state-of-the-art relational learning approaches.

Includes members:
  Member "representation learning on graphs methods and applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to segment 3d point clouds in 2d image space")
  Member "kbqa learning question answering over qa corpora and knowledge bases":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hhh an online medical chatbot system based on knowledge graph and hierarchical bi directional attention")
  Member "knowledge base completion baselines strike back":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a benchmarking study of embedding based entity alignment for knowledge graphs")
  Member "rdf graph alignment with bisimulation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rigid graph alignment")
  Member "probabilistic reasoning via deep learning neural association models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "joint embedding learning of educational knowledge graphs")
  Member "generic statistical relational entity resolution in knowledge graphs":
    also connected through other hyperedges:
      (none)
  Member "cross graph learning of multi relational associations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "analogical inference for multi relational embeddings")
  Member "multilingual knowledge graph embeddings for cross lingual knowledge alignment":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a benchmarking study of embedding based entity alignment for knowledge graphs")

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)