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: "minimax statistical learning with wasserstein distances"
  Abstract: As opposed to standard empirical risk minimization (ERM), distributionally robust optimization aims to minimize the worst-case risk over a larger ambiguity set containing the original empirical distribution of the training data. In this work, we describe a minimax framework for statistical learning with ambiguity sets given by balls in Wasserstein space. In particular, we prove generalization bounds that involve the covering number properties of the original ERM problem. As an illustrative example, we provide generalization guarantees for transport-based domain adaptation problems where the Wasserstein distance between the source and target domain distributions can be reliably estimated from unlabeled samples.

Connected through hyperedge HE-1 (induced by source paper "domain adaptation with conditional distribution matching and generalized label shift"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "domain agnostic learning with disentangled representations"
    - "robust natural language inference models with example forgetting"
    - "batch weight for domain adaptation with mass shift"
    - "learning to transfer examples for partial domain adaptation"
    - "learning representations by maximizing mutual information across views"
    - "regularized learning for domain adaptation under label shifts"
    - "on learning invariant representation for domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "randomization matters how to defend against strong adversarial attacks"):
  with members:
    - "on the hardness of robust classification"
    - "certified adversarial robustness via randomized smoothing"
    - "lower bounds on adversarial robustness from optimal transport"
    - "robust attacks against multiple classifiers"
    - "adversarial risk via optimal transport and optimal couplings"
    - "on evaluating adversarial robustness"
    - "theoretical evidence for adversarial robustness through randomization"
    - "resnets ensemble via the feynman kac formalism to improve natural and robust accuracies"

Connected through hyperedge HE-3 (induced by source paper "invariant risk minimization games"):
  with members:
    - "causal confusion in imitation learning"
    - "agnostic federated learning"
    - "on learning invariant representation for domain adaptation"
    - "finding mixed nash equilibria of generative adversarial networks"
    - "recognition in terra incognita"
    - "domain adaptation by using causal inference to predict invariant conditional distributions"
    - "on causal and anticausal learning"

Connected through hyperedge HE-4 (induced by source paper "a review of domain adaptation without target labels"):
  with members:
    - "regularized learning for domain adaptation under label shifts"
    - "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"
    - "sample to sample correspondence for unsupervised domain adaptation"
    - "unsupervised domain adaptation using regularized hyper graph matching"
    - "theoretical perspective of deep domain adaptation"
    - "deep visual domain adaptation a survey"
    - "characterizing and avoiding negative transfer"
    - "detecting and correcting for label shift with black box predictors"

Connected through hyperedge HE-5 (induced by source paper "generalized domain adaptation with covariate and label shift co alignment"):
  with members:
    - "domain agnostic learning with disentangled representations"
    - "domain adaptation with asymmetrically relaxed distribution alignment"
    - "semi supervised domain adaptation via minimax entropy"
    - "a closer look at few shot classification"
    - "regularized learning for domain adaptation under label shifts"
    - "on learning invariant representation for domain adaptation"
    - "moment matching for multi source domain adaptation"
    - "deep visual domain adaptation a survey"

Connected through hyperedge HE-6 (induced by source paper "addressing model vulnerability to distributional shifts over image transformation sets"):
  with members:
    - "feature critic networks for heterogeneous domain generalization"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "on learning invariant representation for domain adaptation"):
  with members:
    - "regularized learning for domain adaptation under label shifts"
    - "detecting and correcting for label shift with black box predictors"
    - "multi adversarial domain adaptation"
    - "cycada cycle consistent adversarial domain adaptation"
    - "aspect augmented adversarial networks for domain adaptation"
    - "efficient multitask feature and relationship learning"
    - "adversarial discriminative domain adaptation"
    - "learning from simulated and unsupervised images through adversarial training"

Connected through hyperedge HE-8 (induced by source paper "butterfly a panacea for all difficulties in wildly unsupervised domain adaptation"):
  with members:
    - "co teaching robust training of deep neural networks with extremely noisy labels"
    - "cycada cycle consistent adversarial domain adaptation"
    - "maximum classifier discrepancy for unsupervised domain adaptation"
    - "few shot adversarial domain adaptation"
    - "cleannet transfer learning for scalable image classifier training with label noise"
    - "adversarial discriminative domain adaptation"
    - "asymmetric tri training for unsupervised domain adaptation"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"

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)