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: "domain adaptation with asymmetrically relaxed distribution alignment"
  Abstract: Domain adaptation addresses the common problem when the target distribution generating our test data drifts from the source (training) distribution. While absent assumptions, domain adaptation is impossible, strict conditions, e.g. covariate or label shift, enable principled algorithms. Recently-proposed domain-adversarial approaches consist of aligning source and target encodings, often motivating this approach as minimizing two (of three) terms in a theoretical bound on target error. Unfortunately, this minimization can cause arbitrary increases in the third term, e.g. they can break down under shifting label distributions. We propose asymmetrically-relaxed distribution alignment, a new approach that overcomes some limitations of standard domain-adversarial algorithms. Moreover, we characterize precise assumptions under which our algorithm is theoretically principled and demonstrate empirical benefits on both synthetic and real datasets.

Connected through hyperedge HE-1 (induced by source paper "alignflow cycle consistent learning from multiple domains via normalizing flows"):
  with members:
    - "flow improving flow based generative models with variational dequantization and architecture design"
    - "video to video synthesis"
    - "neural autoregressive flows"
    - "universal neural machine translation for extremely low resource languages"
    - "multimodal unsupervised image to image translation"
    - "neural ordinary differential equations"
    - "generalization bounds for unsupervised cross domain mapping with wgans"
    - "ffjord free form continuous dynamics for scalable reversible generative models"

Connected through hyperedge HE-2 (induced by source paper "cluster alignment with a teacher for unsupervised domain adaptation"):
  with members:
    - "on learning invariant representation for domain adaptation"
    - "max mahalanobis linear discriminant analysis networks"
    - "image to image translation for domain adaptation"
    - "cycada cycle consistent adversarial domain adaptation"
    - "structured generative adversarial networks"
    - "maximum classifier discrepancy for unsupervised domain adaptation"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "unsupervised domain adaptation with similarity learning"

Connected through hyperedge HE-3 (induced by source paper "generalized domain adaptation with covariate and label shift co alignment"):
  with members:
    - "domain agnostic learning with disentangled representations"
    - "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"
    - "partial adversarial domain adaptation"

Connected through hyperedge HE-4 (induced by source paper "an adversarial perturbation oriented domain adaptation approach for semantic segmentation"):
  with members:
    - "fully convolutional adaptation networks for semantic segmentation"
    - "learning to adapt structured output space for semantic segmentation"
    - "larger norm more transferable an adaptive feature norm approach for unsupervised domain adaptation"
    - "taking a closer look at domain shift category level adversaries for semantics consistent domain adaptation"
    - "advent adversarial entropy minimization for domain adaptation in semantic segmentation"
    - "generalizing to unseen domains via adversarial data augmentation"
    - "no more discrimination cross city adaptation of road scene segmenters"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-5 (induced by source paper "learning robust global representations by penalizing local predictive power"):
  with members:
    - "domain generalization by solving jigsaw puzzles"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "learning robust representations by projecting superficial statistics out"
    - "do imagenet classifiers generalize to imagenet"
    - "best sources forward domain generalization through source specific nets"
    - "domain invariant projection learning for zero shot recognition"
    - "deep visual domain adaptation a survey"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"

Connected through hyperedge HE-6 (induced by source paper "scl towards accurate domain adaptive object detection via gradient detach based stacked complementary losses"):
  with members:
    - "self training and adversarial background regularization for unsupervised domain adaptive one stage object detection"
    - "towards universal object detection by domain attention"
    - "multi adversarial faster rcnn for unrestricted object detection"
    - "exploring object relation in mean teacher for cross domain detection"
    - "complement objective training"
    - "diversify and match a domain adaptive representation learning paradigm for object detection"
    - "on learning invariant representation for domain adaptation"
    - "semantic foggy scene understanding with synthetic data"

Connected through hyperedge HE-7 (induced by source paper "stabilizing off policy q learning via bootstrapping error reduction"):
  with members:
    - "diagnosing bottlenecks in deep q learning algorithms"
    - "off policy deep reinforcement learning by bootstrapping the covariate shift"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"
    - "striving for simplicity in off policy deep reinforcement learning"
    - "reinforcement learning from imperfect demonstrations"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"

Connected through hyperedge HE-8 (induced by source paper "behavior regularized offline reinforcement learning"):
  with members:
    - "diagnosing bottlenecks in deep q learning algorithms"
    - "stabilizing off policy q learning via bootstrapping error reduction"
    - "off policy evaluation via off policy classification"
    - "dualdice behavior agnostic estimation of discounted stationary distribution corrections"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"
    - "striving for simplicity in off policy deep reinforcement learning"
    - "breaking the curse of horizon infinite horizon off policy estimation"
    - "off policy deep reinforcement learning without exploration"

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)