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 "transparent classification with multilayer logical perceptrons and random binarization"):
  Abstract: Models with transparent inner structure and high classification performance are required to reduce potential risk and provide trust for users in domains like health care, finance, security, etc. However, existing models are hard to simultaneously satisfy the above two properties. In this paper, we propose a new hierarchical rule-based model for classification tasks, named Concept Rule Sets (CRS), which has both a strong expressive ability and a transparent inner structure. To address the challenge of efficiently learning the non-differentiable CRS model, we propose a novel neural network architecture, Multilayer Logical Perceptron (MLLP), which is a continuous version of CRS. Using MLLP and the Random Binarization (RB) method we proposed, we can search the discrete solution of CRS in continuous space using gradient descent and ensure the discrete CRS acts almost the same as the corresponding continuous MLLP. Experiments on 12 public data sets show that CRS outperforms the state-of-the-art approaches and the complexity of the learned CRS is close to the simple decision tree.

Includes members:
  Member "learning algorithms via neural logic networks":
    also connected through other hyperedges:
      (none)
  Member "interpreting cnns via decision trees":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "leveraging rationales to improve human task performance")
  Member "exact and consistent interpretation for piecewise linear neural networks a closed form solution":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "analytic marching an analytic meshing solution from deep implicit surface networks")
  Member "interpreting deep visual representations via network dissection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "self supervised discovering of causal features towards interpretable reinforcement learning")
  Member "interpretable convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "convolutional occupancy networks")
  Member "distilling a neural network into a soft decision tree":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "interpretation and simplification of deep forest")
  Member "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lossless compression of deep neural networks")
  Member "scalable bayesian rule lists":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "interpretation and simplification of deep forest")

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)