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 "a novel neural network model specified for representing logical relations"):
  Abstract: With computers to handle more and more complicated things in variable environments, it becomes an urgent requirement that the artificial intelligence has the ability of automatic judging and deciding according to numerous specific conditions so as to deal with the complicated and variable cases. ANNs inspired by brain is a good candidate. However, most of current numeric ANNs are not good at representing logical relations because these models still try to represent logical relations in the form of ratio based on functional approximation. On the other hand, researchers have been trying to design novel neural network models to make neural network model represent logical relations. In this work, a novel neural network model specified for representing logical relations is proposed and applied. New neurons and multiple kinds of links are defined. Inhibitory links are introduced besides exciting links. Different from current numeric ANNs, one end of an inhibitory link connects an exciting link rather than a neuron. Inhibitory links inhibit the connected exciting links conditionally to make this neural network model represent logical relations correctly. This model can simulate the operations of Boolean logic gates, and construct complex logical relations with the advantages of simpler neural network structures than recent works in this area. This work provides some ideas to make neural networks represent logical relations more directly and efficiently, and the model could be used as the complement to current numeric ANN to deal with logical issues and expand the application areas of ANN.

Includes members:
  Member "deepid3 face recognition with very deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "recursive neural networks can learn logical semantics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "vqa lol visual question answering under the lens of logic")

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)