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: "learning attributes equals multi source domain generalization"
  Abstract: Attributes possess appealing properties and benefit many computer vision problems, such as object recognition, learning with humans in the loop, and image retrieval. Whereas the existing work mainly pursues utilizing attributes for various computer vision problems, we contend that the most basic problem---how to accurately and robustly detect attributes from images---has been left under explored. Especially, the existing work rarely explicitly tackles the need that attribute detectors should generalize well across different categories, including those previously unseen. Noting that this is analogous to the objective of multi-source domain generalization, if we treat each category as a domain, we provide a novel perspective to attribute detection and propose to gear the techniques in multi-source domain generalization for the purpose of learning cross-category generalizable attribute detectors. We validate our understanding and approach with extensive experiments on four challenging datasets and three different problems.

Connected through hyperedge HE-1 (induced by source paper "rethinking zero shot video classification end to end training for realistic applications"):
  with members:
    - "revisiting training strategies and generalization performance in deep metric learning"
    - "action recognition with spatial temporal discriminative filter banks"
    - "divide and conquer the embedding space for metric learning"
    - "action2vec a crossmodal embedding approach to action learning"
    - "cross and learn cross modal self supervision"
    - "informed democracy voting based novelty detection for action recognition"
    - "visual data synthesis via gan for zero shot video classification"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-2 (induced by source paper "deep domain adversarial image generation for domain generalisation"):
  with members:
    - "feature critic networks for heterogeneous domain generalization"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "domain randomization and pyramid consistency simulation to real generalization without accessing target domain data"):
  with members:
    - "bidirectional learning for domain adaptation of semantic segmentation"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "fully convolutional adaptation networks for semantic segmentation"
    - "penalizing top performers conservative loss for semantic segmentation adaptation"
    - "learning to adapt structured output space for semantic segmentation"
    - "a curriculum domain adaptation approach to the semantic segmentation of urban scenes"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"

Connected through hyperedge HE-4 (induced by source paper "constructing self motivated pyramid curriculums for cross domain semantic segmentation a non adversarial approach"):
  with members:
    - "curriculum model adaptation with synthetic and real data for semantic foggy scene understanding"
    - "fully convolutional adaptation networks for semantic segmentation"
    - "penalizing top performers conservative loss for semantic segmentation adaptation"
    - "learning to adapt structured output space for semantic segmentation"
    - "a curriculum domain adaptation approach to the semantic segmentation of urban scenes"
    - "taking a closer look at domain shift category level adversaries for semantics consistent domain adaptation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "advent adversarial entropy minimization for domain adaptation in semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "tarn temporal attentive relation network for few shot and zero shot action recognition"):
  with members:
    - "towards universal representation for unseen action recognition"
    - "prototypical networks for few shot learning"
    - "discriminative convolutional fisher vector network for action recognition"
    - "the kinetics human action video dataset"
    - "learning to compare relation network for few shot learning"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "unsupervised human action detection by action matching"
    - "a decomposable attention model for natural language inference"

Connected through hyperedge HE-6 (induced by source paper "action2vec a crossmodal embedding approach to action learning"):
  with members:
    - "text2shape generating shapes from natural language by learning joint embeddings"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "the kinetics human action video dataset"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "image captioning with semantic attention"
    - "word2visualvec image and video to sentence matching by visual feature prediction"
    - "convolutional two stream network fusion for video action recognition"
    - "multi task zero shot action recognition with prioritised data augmentation"

Connected through hyperedge HE-7 (induced by source paper "transform invariant convolutional neural networks for image classification and search"):
  with members:
    - "ti pooling transformation invariant pooling for feature learning in convolutional neural networks"
    - "interactive inter layer activeness propagation"
    - "jointly modeling embedding and translation to bridge video and language"
    - "contextual action recognition with r cnn"
    - "spatial transformer networks"
    - "deep residual learning for image recognition"
    - "deepfont identify your font from an image"
    - "going deeper with convolutions"

Connected through hyperedge HE-8 (induced by source paper "multi granularity reasoning for social relation recognition from images"):
  with members:
    - "deep reasoning with knowledge graph for social relationship understanding"
    - "simple baselines for human pose estimation and tracking"
    - "exact and consistent interpretation for piecewise linear neural networks a closed form solution"
    - "a domain based approach to social relation recognition"
    - "dual glance model for deciphering social relationships"
    - "mask r cnn"
    - "semantic object parsing with graph lstm"
    - "from facial expression recognition to interpersonal relation prediction"

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)