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 "unsupervised learning by competing hidden units"):
  Abstract: It is widely believed that end-to-end training with the backpropagation algorithm is essential for learning good feature detectors in early layers of artificial neural networks, so that these detectors are useful for the task performed by the higher layers of that neural network. At the same time, the traditional form of backpropagation is biologically implausible. In the present paper we propose an unusual learning rule, which has a degree of biological plausibility and which is motivated by Hebb’s idea that change of the synapse strength should be local—i.e., should depend only on the activities of the pre- and postsynaptic neurons. We design a learning algorithm that utilizes global inhibition in the hidden layer and is capable of learning early feature detectors in a completely unsupervised way. These learned lower-layer feature detectors can be used to train higher-layer weights in a usual supervised way so that the performance of the full network is comparable to the performance of standard feedforward networks trained end-to-end with a backpropagation algorithm on simple tasks.

Includes members:
  Member "assessing the scalability of biologically motivated deep learning algorithms and architectures":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "revisiting spatial invariance with low rank local connectivity")
  Member "biologically motivated algorithms for propagating local target representations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "supervised deep similarity matching")
  Member "dense associative memory is robust to adversarial inputs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "quadratic autoencoder q ae for low dose ct denoising")
  Member "conducting credit assignment by aligning local representations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "supervised deep similarity matching")
  Member "a correlation game for unsupervised learning yields computational interpretations of hebbian excitation anti hebbian inhibition and synapse elimination":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "local unsupervised learning for image analysis")
  Member "dense associative memory for pattern recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hypercomplex valued recurrent correlation neural networks")
  Member "equilibrium propagation bridging the gap between energy based models and backpropagation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep learning in memristive nanowire networks")
  Member "semi supervised learning with ladder networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "ha ccn hierarchical attention based crowd counting network")

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)