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 power of curriculum learning in training deep networks"
  Abstract: Training neural networks is traditionally done by providing a sequence of random mini-batches sampled uniformly from the entire training data. In this work, we analyze the effect of curriculum learning, which involves the non-uniform sampling of mini-batches, on the training of deep networks, and specifically CNNs trained for image recognition. To employ curriculum learning, the training algorithm must resolve 2 problems: (i) sort the training examples by difficulty; (ii) compute a series of mini-batches that exhibit an increasing level of difficulty. We address challenge (i) using two methods: transfer learning from some competitive ``teacher" network, and bootstrapping. In our empirical evaluation, both methods show similar benefits in terms of increased learning speed and improved final performance on test data. We address challenge (ii) by investigating different pacing functions to guide the sampling. The empirical investigation includes a variety of network architectures, using images from CIFAR-10, CIFAR-100 and subsets of ImageNet. We conclude with a novel theoretical analysis of curriculum learning, where we show how it effectively modifies the optimization landscape. We then define the concept of an ideal curriculum, and show that under mild conditions it does not change the corresponding global minimum of the optimization function.

Connected through hyperedge HE-1 (induced by source paper "curriculum deepsdf"):
  with members:
    - "implicit geometric regularization for learning shapes"
    - "learning shape templates with structured implicit functions"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "deep level sets implicit surface representations for 3d shape inference"
    - "learning to infer implicit surfaces without 3d supervision"
    - "deep structured implicit functions"
    - "disn deep implicit surface network for high quality single view 3d reconstruction"
    - "curriculum learning by transfer learning theory and experiments with deep networks"

Connected through hyperedge HE-2 (induced by source paper "curriculum labeling self paced pseudo labeling for semi supervised learning"):
  with members:
    - "semi supervised learning by label gradient alignment"
    - "randaugment practical data augmentation with no separate search"
    - "label propagation for deep semi supervised learning"
    - "mixmatch a holistic approach to semi supervised learning"
    - "autoaugment learning augmentation policies from data"
    - "averaging weights leads to wider optima and better generalization"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "the extreme value machine"

Connected through hyperedge HE-3 (induced by source paper "resource management in wireless networks via multi agent deep reinforcement learning"):
  with members:
    - "spectrum sharing in vehicular networks based on multi agent reinforcement learning"
    - "dota 2 with large scale deep reinforcement learning"
    - "a deep q learning method for downlink power allocation in multi cell networks"
    - "deep reinforcement learning for distributed uncoordinated cognitive radios resource allocation"
    - "zpd teaching strategies for deep reinforcement learning from demonstrations"
    - "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach"
    - "quantifying generalization in reinforcement learning"
    - "power allocation in multi user cellular networks with deep q learning approach"

Connected through hyperedge HE-4 (induced by source paper "diversedepth affine invariant depth prediction using diverse data"):
  with members:
    - "a large rgb d dataset for semi supervised monocular depth estimation"
    - "enforcing geometric constraints of virtual normal for depth prediction"
    - "learning the depths of moving people by watching frozen people"
    - "unsupervised scale consistent depth and ego motion learning from monocular video"
    - "how do neural networks see depth in single images"
    - "learning depth with convolutional spatial propagation network"
    - "deep ordinal regression network for monocular depth estimation"
    - "curriculum learning by transfer learning theory and experiments with deep networks"

Connected through hyperedge HE-5 (induced by source paper "curriculum learning strategies for ir an empirical study on conversation response ranking"):
  with members:
    - "bert with history answer embedding for conversational question answering"
    - "passage re ranking with bert"
    - "introducing mantis a novel multi domain information seeking dialogues dataset"
    - "faq retrieval using query question similarity and bert based query answer relevance"
    - "image difficulty curriculum for generative adversarial networks cugan"
    - "simple applications of bert for ad hoc document retrieval"
    - "a hybrid retrieval generation neural conversation model"
    - "critically examining the neural hype weak baselines and the additivity of effectiveness gains from neural ranking models"

Connected through hyperedge HE-6 (induced by source paper "rethinking curriculum learning with incremental labels and adaptive compensation"):
  with members:
    - "residual networks of residual networks multilevel residual networks"
    - "curriculum learning by transfer learning theory and experiments with deep networks"
    - "experience replay for continual learning"
    - "learning to teach"
    - "shakedrop regularization for deep residual learning"
    - "drop activation implicit parameter reduction and harmonic regularization"
    - "label refinery improving imagenet classification through label progression"
    - "end to end incremental learning"

Connected through hyperedge HE-7 (induced by source paper "fine tuning by curriculum learning for non autoregressive neural machine translation"):
  with members:
    - "non autoregressive machine translation with auxiliary regularization"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "hint based training for non autoregressive machine translation"
    - "fastspeech fast robust and controllable text to speech"
    - "mass masked sequence to sequence pre training for language generation"
    - "non monotonic sequential text generation"
    - "achieving human parity on automatic chinese to english news translation"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"

Connected through hyperedge HE-8 (induced by source paper "all neural networks are created equal"):
  with members:
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "towards understanding learning representations to what extent do different neural networks learn the same representation"
    - "a style based generator architecture for generative adversarial networks"
    - "a convergence analysis of gradient descent for deep linear neural networks"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "understanding deep learning requires rethinking generalization"
    - "densely connected convolutional 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.LG(Machine Learning)