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: "progressive feature alignment for unsupervised domain adaptation"
  Abstract: Unsupervised domain adaptation (UDA) transfers knowledge from a label-rich source domain to a fully-unlabeled target domain. To tackle this task, recent approaches resort to discriminative domain transfer in virtue of pseudo-labels to enforce the class-level distribution alignment across the source and target domains. These methods, however, are vulnerable to the error accumulation and thus incapable of preserving cross-domain category consistency, as the pseudo-labeling accuracy is not guaranteed explicitly. In this paper, we propose the Progressive Feature Alignment Network (PFAN) to align the discriminative features across domains progressively and effectively, via exploiting the intra-class variation in the target domain. To be specific, we first develop an Easy-to-Hard Transfer Strategy (EHTS) and an Adaptive Prototype Alignment (APA) step to train our model iteratively and alternatively. Moreover, upon observing that a good domain adaptation usually requires a non-saturated source classifier, we consider a simple yet efficient way to retard the convergence speed of the source classification loss by further involving a temperature variate into the soft-max function. The extensive experimental results reveal that the proposed PFAN exceeds the state-of-the-art performance on three UDA datasets.

Connected through hyperedge HE-1 (induced by source paper "bi directional generation for unsupervised domain adaptation"):
  with members:
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "adagraph unifying predictive and continuous domain adaptation through graphs"
    - "domain symmetric networks for adversarial domain adaptation"
    - "attending to discriminative certainty for domain adaptation"
    - "transferrable prototypical networks for unsupervised domain adaptation"
    - "unsupervised domain adaptation using feature whitening and consensus loss"
    - "fully convolutional adaptation networks for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "mind the gap enlarging the domain gap in open set domain adaptation"):
  with members:
    - "attract or distract exploit the margin of open set"
    - "weakly supervised open set domain adaptation by dual domain collaboration"
    - "open set domain adaptation by backpropagation"
    - "deep cocktail network multi source unsupervised domain adaptation with category shift"
    - "image to image translation for domain adaptation"
    - "deep hashing network for unsupervised domain adaptation"
    - "cycada cycle consistent adversarial domain adaptation"
    - "maximum classifier discrepancy for unsupervised domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "joint semantic domain alignment and target classifier learning for unsupervised domain adaptation"):
  with members:
    - "m adda unsupervised domain adaptation with deep metric learning"
    - "transferrable prototypical networks for unsupervised domain adaptation"
    - "domain invariant adversarial learning for unsupervised domain adaption"
    - "deepjdot deep joint distribution optimal transport for unsupervised domain adaptation"
    - "domain alignment with triplets"
    - "joint domain alignment and discriminative feature learning for unsupervised deep domain adaptation"
    - "deep adversarial attention alignment for unsupervised domain adaptation the benefit of target expectation maximization"
    - "sampling matters in deep embedding learning"

Connected through hyperedge HE-4 (induced by source paper "category anchor guided unsupervised domain adaptation for semantic segmentation"):
  with members:
    - "famed net a fast and accurate multi scale end to end dehazing network"
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "all about structure adapting structural information across domains for boosting semantic segmentation"
    - "bidirectional learning for domain adaptation of semantic segmentation"
    - "fully point wise convolutional neural network for modeling statistical regularities in natural images"
    - "learning to adapt structured output space for semantic segmentation"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "cross domain weakly supervised object detection through progressive domain adaptation"

Connected through hyperedge HE-5 (induced by source paper "unsupervised domain adaptation via structured prediction based selective pseudo labeling"):
  with members:
    - "unifying unsupervised domain adaptation and zero shot visual recognition"
    - "domain symmetric networks for adversarial domain adaptation"
    - "visual domain adaptation with manifold embedded distribution alignment"
    - "multi adversarial domain adaptation"
    - "joint domain alignment and discriminative feature learning for unsupervised deep domain adaptation"
    - "deep hashing network for unsupervised domain adaptation"
    - "adversarial discriminative domain adaptation"
    - "generate to adapt aligning domains using generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "racial faces in the wild reducing racial bias by information maximization adaptation network"):
  with members:
    - "turning a blind eye explicit removal of biases and variation from deep neural network embeddings"
    - "deep visual domain adaptation a survey"
    - "arcface additive angular margin loss for deep face recognition"
    - "supervised cosmos autoencoder learning beyond the euclidean loss"
    - "deep face recognition a survey"
    - "gender privacy an ensemble of semi adversarial networks for confounding arbitrary gender classifiers"
    - "mind the class weight bias weighted maximum mean discrepancy for unsupervised domain adaptation"
    - "vggface2 a dataset for recognising faces across pose and age"

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.CV(Computer Vision and Pattern Recognition)