human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "learning in the machine the symmetries of the deep learning channel"
  Abstract: In a physical neural system, learning rules must be local both in space and time. In order for learning to occur, non-local information must be communicated to the deep synapses through a communication channel, the deep learning channel. We identify several possible architectures for this learning channel (Bidirectional, Conjoined, Twin, Distinct) and six symmetry challenges: 1) symmetry of architectures; 2) symmetry of weights; 3) symmetry of neurons; 4) symmetry of derivatives; 5) symmetry of processing; and 6) symmetry of learning rules. Random backpropagation (RBP) addresses the second and third symmetry, and some of its variations, such as skipped RBP (SRBP) address the first and the fourth symmetry. Here we address the last two desirable symmetries showing through simulations that they can be achieved and that the learning channel is particularly robust to symmetry variations. Specifically, random backpropagation and its variations can be performed with the same non-linear neurons used in the main input-output forward channel, and the connections in the learning channel can be adapted using the same algorithm used in the forward channel, removing the need for any specialized hardware in the learning channel. Finally, we provide mathematical results in simple cases showing that the learning equations in the forward and backward channels converge to fixed points, for almost any initial conditions. In symmetric architectures, if the weights in both channels are small at initialization, adaptation in both channels leads to weights that are essentially symmetric during and after learning. Biological connections are discussed.

Connected through hyperedge HE-1 (induced by source paper "learning in the machine to share or not to share"):
  with members:
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "learning in the machine random backpropagation and the deep learning channel"
    - "deep learning in neural networks"
    - "deep residual learning for image recognition"
    - "training very deep networks"
    - "going deeper with convolutions"
    - "difference target propagation"
    - "learning spatiotemporal features with 3d convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "error triggered three factor learning dynamics for crossbar arrays"):
  with members:
    - "surrogate gradient learning in spiking neural networks"
    - "a neuromorphic systems approach to in memory computing with non ideal memristive devices from mitigation to exploitation"
    - "synaptic plasticity dynamics for deep continuous local learning"
    - "smplr deep smpl reverse for 3d human pose and shape recovery"
    - "deep supervised learning using local errors"
    - "event driven random back propagation enabling neuromorphic deep learning machines"

Connected through hyperedge HE-3 (induced by source paper "synaptic plasticity dynamics for deep continuous local learning decolle"):
  with members:
    - "surrogate gradient learning in spiking neural networks"
    - "long short term memory and learning to learn in networks of spiking neurons"
    - "slayer spike layer error reassignment in time"
    - "deep supervised learning using local errors"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "decoupled neural interfaces using synthetic gradients"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "training deep spiking neural networks using backpropagation"

Connected through hyperedge HE-4 (induced by source paper "learning in the machine random backpropagation and the deep learning channel"):
  with members:
    - "a theory of local learning the learning channel and the optimality of backpropagation"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "deep residual learning for image recognition"
    - "training very deep networks"
    - "how important is weight symmetry in backpropagation"
    - "going deeper with convolutions"
    - "speech recognition with deep recurrent neural networks"

Question: Please tell me which class the center node 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)