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 "learn to explain efficiently via neural logic inductive learning"):
  Abstract: The capability of making interpretable and self-explanatory decisions is essential for developing responsible machine learning systems. In this work, we study the learning to explain problem in the scope of inductive logic programming (ILP). We propose Neural Logic Inductive Learning (NLIL), an efficient differentiable ILP framework that learns first-order logic rules that can explain the patterns in the data. In experiments, compared with the state-of-the-art methods, we find NLIL can search for rules that are x10 times longer while remaining x3 times faster. We also show that NLIL can scale to large image datasets, i.e. Visual Genome, with 1M entities.

Includes members:
  Member "neural logic machines":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "relational neural machines")
  Member "rotate knowledge graph embedding by relational rotation in complex space":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a benchmarking study of embedding based entity alignment for knowledge graphs")
  Member "gqa a new dataset for real world visual reasoning and compositional question answering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unbiased scene graph generation from biased training")
  Member "explaining explanations in ai":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "exploratory not explanatory counterfactual analysis of saliency maps for deep reinforcement learning")
  Member "tucker tensor factorization for knowledge graph completion":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "knowledge graph embedding for link prediction a comparative analysis")
  Member "inductive logic programming via differentiable deep neural logic networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cln2inv learning loop invariants with continuous logic networks")
  Member "iterative visual reasoning beyond convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adaptive graph convolutional network with attention graph clustering for co saliency detection")
  Member "logical rule induction and theory learning using neural theorem proving":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "neural logic machines")

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.AI(Artificial Intelligence)