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 "fuzzy logic interpretation of artificial neural networks"):
  Abstract: Over past several years, deep learning has achieved huge successes in various applications. However, such a data-driven approach is often criticized for lack of interpretability. Recently, we proposed artificial quadratic neural networks consisting of second-order neurons in potentially many layers. In each second-order neuron, a quadratic function is used in the place of the inner product in a traditional neuron, and then undergoes a nonlinear activation. With a single second-order neuron, any fuzzy logic operation, such as XOR, can be implemented. In this sense, any deep network constructed with quadratic neurons can be interpreted as a deep fuzzy logic system. Since traditional neural networks and second-order counterparts can represent each other and fuzzy logic operations are naturally implemented in second-order neural networks, it is plausible to explain how a deep neural network works with a second-order network as the system model. In this paper, we generalize and categorize fuzzy logic operations implementable with individual second-order neurons, and then perform statistical/information theoretic analyses of exemplary quadratic neural networks.

Includes members:
  Member "geometric understanding of deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an efficient explorative sampling considering the generative boundaries of deep generative neural networks")
  Member "structurally sensitive multi scale deep neural network for low dose ct denoising":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "v2i connectivity based dynamic queue jumper lane for emergency vehicles an approximate dynamic programming approach")
  Member "exact and consistent interpretation for piecewise linear neural networks a closed form solution":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "analytic marching an analytic meshing solution from deep implicit surface networks")
  Member "a new type of neurons for machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "quadratic autoencoder q ae for low dose ct denoising")
  Member "interpreting deep visual representations via network dissection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "self supervised discovering of causal features towards interpretable reinforcement learning")
  Member "revisit fuzzy neural network demystifying batch normalization and relu with generalized hamming network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "optimize tsk fuzzy systems for classification problems mini batch gradient descent with uniform regularization and batch normalization")
  Member "a geometric view of optimal transportation and generative model":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "knowledge representing efficient sparse representation of prior knowledge for knowledge distillation")
  Member "visualizing the loss landscape of neural nets":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "understanding global loss landscape of one hidden layer relu neural 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.NE(Neural and Evolutionary Computing)