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 to teach with dynamic loss functions"
  Abstract: Teaching is critical to human society: it is with teaching that prospective students are educated and human civilization can be inherited and advanced. A good teacher not only provides his/her students with qualified teaching materials (e.g., textbooks), but also sets up appropriate learning objectives (e.g., course projects and exams) considering different situations of a student. When it comes to artificial intelligence, treating machine learning models as students, the loss functions that are optimized act as perfect counterparts of the learning objective set by the teacher. In this work, we explore the possibility of imitating human teaching behaviors by dynamically and automatically outputting appropriate loss functions to train machine learning models. Different from typical learning settings in which the loss function of a machine learning model is predefined and fixed, in our framework, the loss function of a machine learning model (we call it student) is defined by another machine learning model (we call it teacher). The ultimate goal of teacher model is cultivating the student to have better performance measured on development dataset. Towards that end, similar to human teaching, the teacher, a parametric model, dynamically outputs different loss functions that will be used and optimized by its student model at different training stages. We develop an efficient learning method for the teacher model that makes gradient based optimization possible, exempt of the ineffective solutions such as policy optimization. We name our method as "learning to teach with dynamic loss functions" (L2T-DLF for short). Extensive experiments on real world tasks including image classification and neural machine translation demonstrate that our method significantly improves the quality of various student models.

Connected through hyperedge HE-1 (induced by source paper "online meta critic learning for off policy actor critic methods"):
  with members:
    - "feature critic networks for heterogeneous domain generalization"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "addressing the loss metric mismatch with adaptive loss alignment"
    - "learning surrogate losses"
    - "meta gradient reinforcement learning"
    - "soft actor critic algorithms and applications"
    - "meta reinforcement learning of structured exploration strategies"
    - "on learning intrinsic rewards for policy gradient methods"

Connected through hyperedge HE-2 (induced by source paper "optimizing black box metrics with adaptive surrogates"):
  with members:
    - "addressing the loss metric mismatch with adaptive loss alignment"
    - "optimizing generalized rate metrics through game equilibrium"
    - "learning surrogate losses"
    - "unleashing linear optimizers for group fair learning and optimization"
    - "constrained classification and ranking via quantiles"
    - "learning to reweight examples for robust deep learning"
    - "online and stochastic gradient methods for non decomposable loss functions"
    - "optimizing f measure a tale of two approaches"

Connected through hyperedge HE-3 (induced by source paper "learning to generalize from sparse and underspecified rewards"):
  with members:
    - "self supervised generalisation with meta auxiliary learning"
    - "reward shaping via meta learning"
    - "on first order meta learning algorithms"
    - "multi task maximum entropy inverse reinforcement learning"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "discourse aware neural rewards for coherent text generation"
    - "babyai first steps towards grounded language learning with a human in the loop"

Connected through hyperedge HE-4 (induced by source paper "am lfs automl for loss function search"):
  with members:
    - "multi level factorisation net for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "harmonious attention network for person re identification"
    - "cosface large margin cosine loss for deep face recognition"
    - "amc automl for model compression and acceleration on mobile devices"
    - "regularized evolution for image classifier architecture search"
    - "arcface additive angular margin loss for deep face recognition"
    - "gradient harmonized single stage detector"

Connected through hyperedge HE-5 (induced by source paper "addressing the loss metric mismatch with adaptive loss alignment"):
  with members:
    - "autoloss learning discrete schedules for alternate optimization"
    - "deep bilevel learning"
    - "cosface large margin cosine loss for deep face recognition"
    - "snas stochastic neural architecture search"
    - "learning to reweight examples for robust deep learning"
    - "deep metric learning with hierarchical triplet loss"
    - "attention based ensemble for deep metric learning"
    - "sampling matters in deep embedding learning"

Connected through hyperedge HE-6 (induced by source paper "self regulated interactive sequence to sequence learning"):
  with members:
    - "reinforcement learning based curriculum optimization for neural machine translation"
    - "joey nmt a minimalist nmt toolkit for novices"
    - "reliability and learnability of human bandit feedback for sequence to sequence reinforcement learning"
    - "a reinforcement learning approach to interactive predictive neural machine translation"
    - "active learning for interactive neural machine translation of data streams"
    - "learning from chunk based feedback in neural machine translation"
    - "a call for clarity in reporting bleu scores"
    - "semi supervised sequence modeling with cross view training"

Connected through hyperedge HE-7 (induced by source paper "viable fast adaptation via backpropagating learned loss"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "alpha maml adaptive model agnostic meta learning"
    - "meta learning via learned loss"
    - "fast context adaptation via meta learning"
    - "how to train your maml"
    - "evolved policy gradients"
    - "probabilistic model agnostic meta learning"
    - "one shot visual imitation learning via meta learning"

Connected through hyperedge HE-8 (induced by source paper "push the student to learn right progressive gradient correcting by meta learner on corrupted labels"):
  with members:
    - "class balanced loss based on effective number of samples"
    - "on first order meta learning algorithms"
    - "gradient agreement as an optimization objective for meta learning"
    - "dimensionality driven learning with noisy labels"
    - "co teaching robust training of deep neural networks with extremely noisy labels"
    - "learning to reweight examples for robust deep learning"
    - "using trusted data to train deep networks on labels corrupted by severe noise"
    - "small sample learning in big data era"

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)