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: "curriculum learning by transfer learning theory and experiments with deep networks"
  Abstract: Our first contribution in this paper is a theoretical investigation of curriculum learning in the context of stochastic gradient descent when optimizing the least squares loss function. We prove that the rate of convergence of an ideal curriculum learning method in monotonically increasing with the difficulty of the examples, and that this increase in convergence rate is monotonically decreasing as training proceeds. In our second contribution we analyze curriculum learning in the context of training a CNN for image classification. Here one crucial problem is the means to achieve a curriculum. We describe a method which infers the curriculum by way of transfer learning from another network, pre-trained on a different task. While this approach can only approximate the ideal curriculum, we observe empirically similar behavior to the one predicted by the theory, namely, a significant boost in convergence speed at the beginning of training. When the task is made more difficult, improvement in generalization performance is observed. Finally, curriculum learning exhibits robustness against unfavorable conditions such as strong regularization.

Connected through hyperedge HE-1 (induced by source paper "curriculum learning for reinforcement learning domains a framework and survey"):
  with members:
    - "emergent tool use from multi agent autocurricula"
    - "an optimization framework for task sequencing in curriculum learning"
    - "curriculum learning for cumulative return maximization"
    - "evolutionarily curated curriculum learning for deep reinforcement learning agents"
    - "starcraft micromanagement with reinforcement learning and curriculum transfer learning"
    - "learning curriculum policies for reinforcement learning"
    - "screenernet learning self paced curriculum for deep neural networks"
    - "theory of curriculum learning with convex loss functions"

Connected through hyperedge HE-2 (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"
    - "on the power of curriculum learning in training deep networks"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "learning to inpaint by progressively growing the mask regions"):
  with members:
    - "foreground aware image inpainting"
    - "competence based curriculum learning for neural machine translation"
    - "improved training with curriculum gans"
    - "free form image inpainting with gated convolution"
    - "a style based generator architecture for generative adversarial networks"
    - "generative image inpainting with contextual attention"
    - "curriculum learning and minibatch bucketing in neural machine translation"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-4 (induced by source paper "learning from easy to complex adaptive multi curricula learning for neural dialogue generation"):
  with members:
    - "improving neural conversational models with entropy based data filtering"
    - "recosa detecting the relevant contexts with self attention for multi turn dialogue generation"
    - "what makes a good conversation how controllable attributes affect human judgments"
    - "competence based curriculum learning for neural machine translation"
    - "simple and effective curriculum pointer generator networks for reading comprehension over long narratives"
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "better conversations by modeling filtering and optimizing for coherence and diversity"
    - "personalizing dialogue agents i have a dog do you have pets too"

Connected through hyperedge HE-5 (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"
    - "on the power of curriculum learning in training deep networks"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "meta transfer learning through hard tasks"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "lgm net learning to generate matching networks for few shot learning"
    - "a closer look at few shot classification"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "adapted deep embeddings a synthesis of methods for k shot inductive transfer learning"
    - "meta learning with latent embedding optimization"
    - "learning to propagate labels transductive propagation network for few shot learning"

Connected through hyperedge HE-7 (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"
    - "on the power of curriculum learning in training deep networks"
    - "image difficulty curriculum for generative adversarial networks cugan"
    - "simple applications of bert for ad hoc document retrieval"
    - "a hybrid retrieval generation neural conversation model"

Connected through hyperedge HE-8 (induced by source paper "curriculum based dropout discriminator for domain adaptation"):
  with members:
    - "beyond sharing weights for deep domain adaptation"
    - "attending to discriminative certainty for domain adaptation"
    - "looking back at labels a class based domain adaptation technique"
    - "differential attention for visual question answering"
    - "importance weighted adversarial nets for partial domain adaptation"
    - "improved training with curriculum gans"
    - "multi adversarial domain adaptation"
    - "dual discriminator generative adversarial nets"

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)