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 "putting a bug in ml the moth olfactory network learns to read mnist"):
  Abstract: We seek to (i) characterize the learning architectures exploited in biological neural networks for training on very few samples, and (ii) port these algorithmic structures to a machine learning context. The Moth Olfactory Network is among the simplest biological neural systems that can learn, and its architecture includes key structural elements widespread in biological neural nets, such as cascaded networks, competitive inhibition, high intrinsic noise, sparsity, reward mechanisms, and Hebbian plasticity. The interactions of these structural elements play a critical enabling role in rapid learning. #R##N#We assign a computational model of the Moth Olfactory Network the task of learning to read the MNIST digits. This model, MothNet, is closely aligned with the moth's known biophysics and with in vivo electrode data, including data collected from moths learning new odors. We show that MothNet successfully learns to read given very few training samples (1 to 20 samples per class). In this few-samples regime, it substantially outperforms standard machine learning methods such as nearest-neighbors, support-vector machines, and convolutional neural networks (CNNs). The MothNet architecture illustrates how our proposed algorithmic structures, derived from biological brains, can be used to build alternative deep neural nets (DNNs) that may potentially avoid some of DNNs current learning rate limitations. This novel, bio-inspired neural network architecture offers a valuable complementary approach to DNN design.

Includes members:
  Member "active one shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reinforced active learning for image segmentation")
  Member "one shot learning with memory augmented neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "incremental few shot object detection")
  Member "matching networks for one shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "deep learning in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "π nets deep polynomial neural networks")
  Member "semi supervised learning with ladder networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "k sparse autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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.LG(Machine Learning)