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: "feature critic networks for heterogeneous domain generalization"
  Abstract: The well known domain shift issue causes model performance to degrade when deployed to a new target domain with different statistics to training. Domain adaptation techniques alleviate this, but need some instances from the target domain to drive adaptation. Domain generalisation is the recently topical problem of learning a model that generalises to unseen domains out of the box, and various approaches aim to train a domain-invariant feature extractor, typically by adding some manually designed losses. In this work, we propose a learning to learn approach, where the auxiliary loss that helps generalisation is itself learned. Beyond conventional domain generalisation, we consider a more challenging setting of heterogeneous domain generalisation, where the unseen domains do not share label space with the seen ones, and the goal is to train a feature representation that is useful off-the-shelf for novel data and novel categories. Experimental evaluation demonstrates that our method outperforms state-of-the-art solutions in both settings.

Connected through hyperedge HE-1 (induced by source paper "revisiting meta learning as supervised learning"):
  with members:
    - "task2vec task embedding for meta learning"
    - "a closer look at few shot classification"
    - "the information complexity of learning tasks their structure and their distance"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "meta learning a survey"
    - "meta learning with latent embedding optimization"
    - "meta learning transferable active learning policies by deep reinforcement learning"
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "cross domain few shot classification via learned feature wise transformation"):
  with members:
    - "crdoco pixel level domain transfer with cross domain consistency"
    - "dense classification and implanting for few shot learning"
    - "meta learning with differentiable convex optimization"
    - "progressive domain adaptation for object detection"
    - "a closer look at few shot classification"
    - "semantic image synthesis with spatially adaptive normalization"
    - "guided image to image translation with bi directional feature transformation"
    - "few shot viewpoint estimation"

Connected through hyperedge HE-3 (induced by source paper "deep domain adversarial image generation for domain generalisation"):
  with members:
    - "domain randomization and pyramid consistency simulation to real generalization without accessing target domain data"
    - "model vulnerability to distributional shifts over image transformation sets"
    - "episodic training for domain generalization"
    - "domain generalization by solving jigsaw puzzles"
    - "torchreid a library for deep learning person re identification in pytorch"
    - "learning generalisable omni scale representations for person re identification"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "generalizing across domains via cross gradient training"

Connected through hyperedge HE-4 (induced by source paper "online meta critic learning for off policy actor critic methods"):
  with members:
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "addressing the loss metric mismatch with adaptive loss alignment"
    - "learning surrogate losses"
    - "learning to teach with dynamic loss functions"
    - "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-5 (induced by source paper "angular visual hardness"):
  with members:
    - "confidence regularized self training"
    - "compressive hyperspherical energy minimization"
    - "do imagenet classifiers generalize to imagenet"
    - "decoupled networks"
    - "reducing overconfident errors outside the known distribution"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "learning towards minimum hyperspherical energy"
    - "not all samples are created equal deep learning with importance sampling"

Connected through hyperedge HE-6 (induced by source paper "frustratingly easy person re identification generalizing person re id in practice"):
  with members:
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "eanet enhancing alignment for cross domain person re identification"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "multi task mid level feature alignment network for unsupervised cross dataset person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "batch instance normalization for adaptively style invariant neural networks"
    - "ring loss convex feature normalization for face recognition"

Connected through hyperedge HE-7 (induced by source paper "adversarial pyramid network for video domain generalization"):
  with members:
    - "temporal attentive alignment for video domain adaptation"
    - "stm spatiotemporal and motion encoding for action recognition"
    - "domain generalization by solving jigsaw puzzles"
    - "spatiotemporal pyramid network for video action recognition"
    - "domain generalization via model agnostic learning of semantic features"
    - "generalizing across domains via cross gradient training"
    - "generalizing to unseen domains via adversarial data augmentation"
    - "tsm temporal shift module for efficient video understanding"

Connected through hyperedge HE-8 (induced by source paper "addressing model vulnerability to distributional shifts over image transformation sets"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "episodic training for domain generalization"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "do imagenet classifiers generalize to imagenet"
    - "semantic adversarial examples"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "autoaugment learning augmentation policies from data"
    - "generalizing across domains via cross gradient training"

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)