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 by mixture of alignments of second or higher order scatter tensors"
  Abstract: In this paper, we propose an approach to the domain adaptation, dubbed Second- or Higher-order Transfer of Knowledge (So-HoT), based on the mixture of alignments of second- or higher-order scatter statistics between the source and target domains. The human ability to learn from few labeled samples is a recurring motivation in the literature for domain adaptation. Towards this end, we investigate the supervised target scenario for which few labeled target training samples per category exist. Specifically, we utilize two CNN streams: the source and target networks fused at the classifier level. Features from the fully connected layers fc7 of each network are used to compute second- or even higher-order scatter tensors; one per network stream per class. As the source and target distributions are somewhat different despite being related, we align the scatters of the two network streams of the same class (within-class scatters) to a desired degree with our bespoke loss while maintaining good separation of the between-class scatters. We train the entire network in end-to-end fashion. We provide evaluations on the standard Office benchmark (visual domains), RGB-D combined with Caltech256 (depth-to-rgb transfer) and Pascal VOC2007 combined with the TU Berlin dataset (image-to-sketch transfer). We attain state-of-the-art results.

Connected through hyperedge HE-1 (induced by source paper "a balanced and uncertainty aware approach for partial domain adaptation"):
  with members:
    - "complement objective training"
    - "tackling partial domain adaptation with self supervision"
    - "learning to transfer examples for partial domain adaptation"
    - "virtual mixup training for unsupervised domain adaptation"
    - "transfer adaptation learning a decade survey"
    - "semi supervised learning by augmented distribution alignment"
    - "characterizing and avoiding negative transfer"
    - "importance weighted adversarial nets for partial domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "few shot action recognition via improved attention with self supervision"):
  with members:
    - "few shot learning via saliency guided hallucination of samples"
    - "meta learning with differentiable convex optimization"
    - "few shot learning with localization in realistic settings"
    - "boosting few shot visual learning with self supervision"
    - "edge labeling graph neural network for few shot learning"
    - "generating classification weights with gnn denoising autoencoders for few shot learning"
    - "boosting supervision with self supervision for few shot learning"
    - "moments in time dataset one million videos for event understanding"

Connected through hyperedge HE-3 (induced by source paper "few shot learning with multi scale self supervision"):
  with members:
    - "few shot learning via saliency guided hallucination of samples"
    - "meta learning with differentiable convex optimization"
    - "few shot learning with localization in realistic settings"
    - "edge labeling graph neural network for few shot learning"
    - "generating classification weights with gnn denoising autoencoders for few shot learning"
    - "hallucinating idt descriptors and i3d optical flow features for action recognition with cnns"
    - "meta learning with latent embedding optimization"
    - "how to train your maml"

Connected through hyperedge HE-4 (induced by source paper "open set domain adaptation for image and action recognition"):
  with members:
    - "mind the class weight bias weighted maximum mean discrepancy for unsupervised domain adaptation"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "deep hashing network for unsupervised domain adaptation"
    - "when unsupervised domain adaptation meets tensor representations"
    - "adversarial discriminative domain adaptation"
    - "unified deep supervised domain adaptation and generalization"
    - "the kinetics human action video dataset"
    - "visda the visual domain adaptation challenge"

Connected through hyperedge HE-5 (induced by source paper "learning domain adaptive features with unlabeled domain bridges"):
  with members:
    - "domain agnostic learning with disentangled representations"
    - "blending target domain adaptation by adversarial meta adaptation networks"
    - "transferrable prototypical networks for unsupervised domain adaptation"
    - "moment matching for multi source domain adaptation"
    - "a unified feature disentangler for multi domain image translation and manipulation"
    - "unsupervised multi target domain adaptation an information theoretic approach"
    - "deep cocktail network multi source unsupervised domain adaptation with category shift"
    - "dlow domain flow for adaptation and generalization"

Connected through hyperedge HE-6 (induced by source paper "on minimum discrepancy estimation for deep domain adaptation"):
  with members:
    - "boosting domain adaptation by discovering latent domains"
    - "a deep four stream siamese convolutional neural network with joint verification and identification loss for person re detection"
    - "mind the class weight bias weighted maximum mean discrepancy for unsupervised domain adaptation"
    - "image to image translation for domain adaptation"
    - "deep hashing network for unsupervised domain adaptation"
    - "maximum classifier discrepancy for unsupervised domain adaptation"
    - "unsupervised domain adaptation with similarity learning"
    - "adversarial feature augmentation for unsupervised domain adaptation"

Connected through hyperedge HE-7 (induced by source paper "beyond sharing weights for deep domain adaptation"):
  with members:
    - "progressive neural networks"
    - "adversarial discriminative domain adaptation"
    - "deep reconstruction classification networks for unsupervised domain adaptation"
    - "cross stitch networks for multi task learning"
    - "correlation alignment for unsupervised domain adaptation"
    - "deep transfer learning with joint adaptation networks"
    - "domain separation networks"
    - "unsupervised domain adaptation with residual transfer networks"

Connected through hyperedge HE-8 (induced by source paper "correlation aware adversarial domain adaptation and generalization"):
  with members:
    - "episodic training for domain generalization"
    - "multi component image translation for deep domain generalization"
    - "autodial automatic domain alignment layers"
    - "deep hashing network for unsupervised domain adaptation"
    - "adversarial discriminative domain adaptation"
    - "unified deep supervised domain adaptation and generalization"
    - "minimal entropy correlation alignment for unsupervised deep domain adaptation"
    - "deeper broader and artier domain generalization"

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)