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: "self supervised generalisation with meta auxiliary learning"
  Abstract: Learning with auxiliary tasks can improve the ability of a primary task to generalise. However, this comes at the cost of manually labelling auxiliary data. We propose a new method which automatically learns appropriate labels for an auxiliary task, such that any supervised learning task can be improved without requiring access to any further data. The approach is to train two neural networks: a label-generation network to predict the auxiliary labels, and a multi-task network to train the primary task alongside the auxiliary task. The loss for the label-generation network incorporates the loss of the multi-task network, and so this interaction between the two networks can be seen as a form of meta learning with a double gradient. We show that our proposed method, Meta AuXiliary Learning (MAXL), outperforms single-task learning on 7 image datasets, without requiring any additional data. We also show that MAXL outperforms several other baselines for generating auxiliary labels, and is even competitive when compared with human-defined auxiliary labels. The self-supervised nature of our method leads to a promising new direction towards automated generalisation. Source code can be found at https://github.com/lorenmt/maxl.

Connected through hyperedge HE-1 (induced by source paper "learning to generalize one sample at a time with self supervision"):
  with members:
    - "episodic training for domain generalization"
    - "adagraph unifying predictive and continuous domain adaptation through graphs"
    - "domain generalization by solving jigsaw puzzles"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "grasp2vec learning object representations from self supervised grasping"
    - "boosting self supervised learning via knowledge transfer"
    - "open set domain adaptation by backpropagation"
    - "deep clustering for unsupervised learning of visual features"

Connected through hyperedge HE-2 (induced by source paper "learning to generalize from sparse and underspecified rewards"):
  with members:
    - "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"
    - "learning to teach with dynamic loss functions"

Connected through hyperedge HE-3 (induced by source paper "vision language navigation with self supervised auxiliary reasoning tasks"):
  with members:
    - "test time training for out of distribution generalization"
    - "vision and dialog navigation"
    - "lxmert learning cross modality encoder representations from transformers"
    - "fusion of detected objects in text for visual question answering"
    - "scene graph generation with external knowledge and image reconstruction"
    - "discovery of useful questions as auxiliary tasks"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "albert a lite bert for self supervised learning of language representations"

Connected through hyperedge HE-4 (induced by source paper "semantic segmentation from remote sensor data and the exploitation of latent learning for classification of auxiliary tasks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "temporary removal aerial imagery for roof segmentation a large scale dataset towards automatic mapping of buildings"
    - "fine grained visual categorization using meta learning optimization with sample selection of auxiliary data"
    - "automatic pixelwise object labeling for aerial imagery using stacked u nets"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "auxiliary tasks in multi task learning"
    - "ternausnetv2 fully convolutional network for instance segmentation"
    - "concurrent spatial and channel squeeze excitation in fully convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "optimizing data usage via differentiable rewards"):
  with members:
    - "revisiting low resource neural machine translation a case study"
    - "target conditioned sampling optimizing data selection for multilingual neural machine translation"
    - "reinforcement learning based curriculum optimization for neural machine translation"
    - "competence based curriculum learning for neural machine translation"
    - "dynamically composing domain data selection with clean data selection by co curricular learning for neural machine translation"
    - "multilingual neural machine translation with soft decoupled encoding"
    - "do better imagenet models transfer better"
    - "reinforced co training"

Connected through hyperedge HE-6 (induced by source paper "to balance or not to balance a simple yet effective approach for learning with long tailed distributions"):
  with members:
    - "learning imbalanced datasets with label distribution aware margin loss"
    - "boosting few shot visual learning with self supervision"
    - "dynamic curriculum learning for imbalanced data classification"
    - "large scale long tailed recognition in an open world"
    - "class balanced loss based on effective number of samples"
    - "revisiting self supervised visual representation learning"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "auxiliary tasks in multi task learning"

Connected through hyperedge HE-7 (induced by source paper "analyzing the cross sensor portability of neural network architectures for lidar based semantic labeling"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "pointpillars fast encoders for object detection from point clouds"
    - "boosting lidar based semantic labeling by cross modal training data generation"
    - "pointseg real time semantic segmentation based on 3d lidar point cloud"
    - "birdnet a 3d object detection framework from lidar information"
    - "semantic segmentation of 3d lidar data in dynamic scene using semi supervised learning"

Connected through hyperedge HE-8 (induced by source paper "meta neighborhoods"):
  with members:
    - "meta curvature"
    - "adaptive posterior learning few shot learning with a surprise based memory module"
    - "a closer look at few shot classification"
    - "robust classification with convolutional prototype learning"
    - "meta learning with differentiable closed form solvers"
    - "memory matching networks for one shot image recognition"
    - "prototypical networks for few shot learning"
    - "learning to remember rare events"

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)