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 "insect cyborgs biological feature generators improve machine learning accuracy on limited data"):
  Abstract: Despite many successes, machine learning (ML) methods such as neural nets often struggle to learn given small training sets. In contrast, biological neural nets (BNNs) excel at fast learning. We can thus look to BNNs for tools to improve performance of ML methods in this low-data regime. #R##N#The insect olfactory network, though simple, can learn new odors very rapidly. Its two key structures are a layer with competitive inhibition (the Antennal Lobe, AL), followed by a high dimensional sparse plastic layer (the Mushroom Body, MB). This AL-MB network can rapidly learn not only odors but also handwritten digits, better in fact than standard ML methods in the few-shot regime. #R##N#In this work, we deploy the AL-MB network as an automatic feature generator, using its Readout Neurons as additional features for standard ML classifiers. We hypothesize that the AL-MB structure has a strong intrinsic clustering ability; and that its Readout Neurons, used as input features, will boost the performance of ML methods. #R##N#We find that these "insect cyborgs", ie classifiers that are part-moth and part-ML method, deliver significantly better performance than baseline ML methods alone on a generic (non-spatial) 85-feature, 10-class task derived from the MNIST dataset. Accuracy improves by an average of 6% to 33% for N   15. Remarkably, these moth-generated features increase ML accuracy even when the ML method's baseline accuracy already exceeds the AL-MB's own limited capacity. #R##N#The two structures in the AL-MB, a competitive inhibition layer and a high-dimensional sparse layer with Hebbian plasticity, are novel in the context of artificial NNs but endemic in BNNs. We believe they can be deployed either prepended as feature generators or inserted as layers into deep NNs, to potentially improve ML performance.

Includes members:
  Member "putting a bug in ml the moth olfactory network learns to read mnist":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "biologically plausible deep learning but how far can we go with shallow networks")
  Member "k sparse autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "human to ai coach improving human inputs to ai systems")

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.ET(Emerging Technologies)