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: "on the origin of deep learning"
  Abstract: This paper is a review of the evolutionary history of deep learning models. It covers from the genesis of neural networks when associationism modeling of the brain is studied, to the models that dominate the last decade of research in deep learning like convolutional neural networks, deep belief networks, and recurrent neural networks. In addition to a review of these models, this paper primarily focuses on the precedents of the models above, examining how the initial ideas are assembled to construct the early models and how these preliminary models are developed into their current forms. Many of these evolutionary paths last more than half a century and have a diversity of directions. For example, CNN is built on prior knowledge of biological vision system; DBN is evolved from a trade-off of modeling power and computation complexity of graphical models and many nowadays models are neural counterparts of ancient linear models. This paper reviews these evolutionary paths and offers a concise thought flow of how these models are developed, and aims to provide a thorough background for deep learning. More importantly, along with the path, this paper summarizes the gist behind these milestones and proposes many directions to guide the future research of deep learning.

Connected through hyperedge HE-1 (induced by source paper "high frequency component helps explain the generalization of convolutional neural networks"):
  with members:
    - "on the effectiveness of low frequency perturbations"
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "theoretically principled trade off between robustness and accuracy"
    - "batch normalization is a cause of adversarial vulnerability"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "learning robust representations by projecting superficial statistics out"
    - "a simple explanation for the existence of adversarial examples with small hamming distance"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"

Connected through hyperedge HE-2 (induced by source paper "deep octonion networks"):
  with members:
    - "convolutional neural networks with alternately updated clique"
    - "deep quaternion networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "an overview of multi task learning in deep neural networks"
    - "aggregated residual transformations for deep neural networks"
    - "fractalnet ultra deep neural networks without residuals"
    - "recent advances in convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "evolving neural networks in reinforcement learning by means of umdac"):
  with members:
    - "playing fps games with deep reinforcement learning"
    - "continuous control with deep reinforcement learning"
    - "adam a method for stochastic optimization"
    - "playing atari with deep reinforcement learning"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-4 (induced by source paper "accelerating deterministic and stochastic binarized neural networks on fpgas using opencl"):
  with members:
    - "a gpu outperforming fpga accelerator architecture for binary convolutional neural networks"
    - "binarized convolutional neural networks with separable filters for efficient hardware acceleration"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "deep learning research landscape roadmap in a nutshell past present and future towards deep cortical learning"):
  with members:
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "google s multilingual neural machine translation system enabling zero shot translation"

Connected through hyperedge HE-6 (induced by source paper "encoding candlesticks as images for patterns classification using convolutional neural networks"):
  with members:
    - "deep learning for time series analysis"

Connected through hyperedge HE-7 (induced by source paper "recent advances in deep learning an overview"):
  with members:
    - "deep learning a critical appraisal"
    - "adversarial attacks on neural network policies"
    - "deep learning for environmentally robust speech recognition an overview of recent developments"
    - "lstm a search space odyssey"
    - "a knowledge grounded neural conversation model"
    - "me r cnn multi expert region based cnn for object detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "dynamic routing between capsules"

Connected through hyperedge HE-8 (induced by source paper "overcoming catastrophic forgetting with hard attention to the task"):
  with members:
    - "packnet adding multiple tasks to a single network by iterative pruning"
    - "progressive neural networks"
    - "lifelong learning with dynamically expandable networks"
    - "overcoming catastrophic interference by conceptors"
    - "continual learning with deep generative replay"
    - "overcoming catastrophic forgetting by incremental moment matching"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

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.LG(Machine Learning)