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: "autoloss learning discrete schedules for alternate optimization"
  Abstract: Many machine learning problems involve iteratively and alternately optimizing different task objectives with respect to different sets of parameters. Appropriately scheduling the optimization of a task objective or a set of parameters is usually crucial to the quality of convergence. In this paper, we present AutoLoss, a meta-learning framework that automatically learns and determines the optimization schedule. AutoLoss provides a generic way to represent and learn the discrete optimization schedule from metadata, allows for a dynamic and data-driven schedule in ML problems that involve alternating updates of different parameters or from different loss objectives. We apply AutoLoss on four ML tasks: d-ary quadratic regression, classification using a multi-layer perceptron (MLP), image generation using GANs, and multi-task neural machine translation (NMT). We show that the AutoLoss controller is able to capture the distribution of better optimization schedules that result in higher quality of convergence on all four tasks. The trained AutoLoss controller is generalizable -- it can guide and improve the learning of a new task model with different specifications, or on different datasets.

Connected through hyperedge HE-1 (induced by source paper "blending target domain adaptation by adversarial meta adaptation networks"):
  with members:
    - "unbiasing semantic segmentation for robot perception using synthetic data feature transfer"
    - "real to virtual domain unification for end to end autonomous driving"
    - "unsupervised multi target domain adaptation an information theoretic approach"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "multi target unsupervised domain adaptation without exactly shared categories"
    - "deep cocktail network multi source unsupervised domain adaptation with category shift"
    - "boosting domain adaptation by discovering latent domains"
    - "low shot learning from imaginary data"

Connected through hyperedge HE-2 (induced by source paper "addressing the loss metric mismatch with adaptive loss alignment"):
  with members:
    - "deep bilevel learning"
    - "learning to teach with dynamic loss functions"
    - "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-3 (induced by source paper "teacher supervises students how to learn from partially labeled images for facial landmark detection"):
  with members:
    - "network pruning via transformable architecture search"
    - "few example object detection with model communication"
    - "prototype propagation networks ppn for weakly supervised few shot learning on category graph"
    - "quantized densely connected u nets for efficient landmark localization"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "exploring disentangled feature representation beyond face identification"
    - "style aggregated network for facial landmark detection"
    - "learning to reweight examples for robust deep learning"

Connected through hyperedge HE-4 (induced by source paper "adaptive scheduling for multi task learning"):
  with members:
    - "deep contextualized word representations"
    - "end to end multi task learning with attention"
    - "multi task learning as multi objective optimization"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "scheduled multi task learning from syntax to translation"
    - "automated curriculum learning for neural networks"
    - "teacher student curriculum learning"
    - "online learning rate adaptation with hypergradient descent"

Connected through hyperedge HE-5 (induced by source paper "softadapt techniques for adaptive loss weighting of neural networks with multi part loss functions"):
  with members:
    - "addressing the loss metric mismatch with adaptive loss alignment"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "a general and adaptive robust loss function"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks"
    - "yolo9000 better faster stronger"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "an overview of gradient descent optimization 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)