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 "answering visual relational queries in web extracted knowledge graphs"):
  Abstract: A visual-relational knowledge graph (KG) is a multi-relational graph whose entities are associated with images. We explore novel machine learning approaches for answering visual-relational queries in web-extracted knowledge graphs. To this end, we have created ImageGraph, a KG with 1,330 relation types, 14,870 entities, and 829,931 images crawled from the web. With visual-relational KGs such as ImageGraph one can introduce novel probabilistic query types in which images are treated as first-class citizens. Both the prediction of relations between unseen images as well as multi-relational image retrieval can be expressed with specific families of visual-relational queries. We introduce novel combinations of convolutional networks and knowledge graph embedding methods to answer such queries. We also explore a zero-shot learning scenario where an image of an entirely new entity is linked with multiple relations to entities of an existing KG. The resulting multi-relational grounding of unseen entity images into a knowledge graph serves as a semantic entity representation. We conduct experiments to demonstrate that the proposed methods can answer these visual-relational queries efficiently and accurately.

Includes members:
  Member "embedding multimodal relational data for knowledge base completion":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a new codebook design for analog beamforming in millimeter wave communication")
  Member "know evolve deep temporal reasoning for dynamic knowledge graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to simulate complex physics with graph networks")
  Member "learning to disambiguate by asking discriminative questions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "why does a visual question have different answers")
  Member "deep metric learning with angular loss":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "revisiting training strategies and generalization performance in deep metric learning")
  Member "a neural knowledge language model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cotk an open source toolkit for fast development and fair evaluation of text generation")
  Member "knowledge base completion baselines strike back":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a benchmarking study of embedding based entity alignment for knowledge graphs")
  Member "generating factoid questions with recurrent neural networks the 30m factoid question answer corpus":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "asking questions the human way scalable question answer generation from text corpus")
  Member "deep metric learning via facility location":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "opengan open set generative adversarial 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.LG(Machine Learning)