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: "fine grained recognition in the wild a multi task domain adaptation approach"
  Abstract: While fine-grained object recognition is an important problem in computer vision, current models are unlikely to accurately classify objects in the wild. These fully supervised models need additional annotated images to classify objects in every new scenario, a task that is infeasible. However, sources such as e-commerce websites and field guides provide annotated images for many classes. In this work, we study fine-grained domain adaptation as a step towards overcoming the dataset shift between easily acquired annotated images and the real world. Adaptation has not been studied in the fine-grained setting where annotations such as attributes could be used to increase performance. Our work uses an attribute based multi-task adaptation loss to increase accuracy from a baseline of 4.1% to 19.1% in the semi-supervised adaptation case. Prior do- main adaptation works have been benchmarked on small datasets such as [46] with a total of 795 images for some domains, or simplistic datasets such as [41] consisting of digits. We perform experiments on a subset of a new challenging fine-grained dataset consisting of 1,095,021 images of 2, 657 car categories drawn from e-commerce web- sites and Google Street View.

Connected through hyperedge HE-1 (induced by source paper "madan multi source adversarial domain aggregation network for domain adaptation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "domain randomization and pyramid consistency simulation to real generalization without accessing target domain data"
    - "multi source distilling domain adaptation"
    - "moment matching for multi source domain adaptation"
    - "learning to adapt structured output space for semantic segmentation"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "algorithms and theory for multiple source adaptation"

Connected through hyperedge HE-2 (induced by source paper "multi source domain adaptation in the deep learning era a systematic survey"):
  with members:
    - "multi source domain adaptation for text classification via distancenet bandits"
    - "multi source domain adaptation for visual sentiment classification"
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "domain randomization and pyramid consistency simulation to real generalization without accessing target domain data"
    - "multi source domain adaptation for semantic segmentation"
    - "multi source distilling domain adaptation"
    - "moment matching for multi source domain adaptation"
    - "learning to adapt structured output space for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "a robust learning approach to domain adaptive object detection"):
  with members:
    - "semantic foggy scene understanding with synthetic data"
    - "cross domain weakly supervised object detection through progressive domain adaptation"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "joint optimization framework for learning with noisy labels"
    - "learning to reweight examples for robust deep learning"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "advent adversarial entropy minimization for domain adaptation in semantic segmentation"
    - "image to image translation for domain adaptation"

Connected through hyperedge HE-4 (induced by source paper "recognizing part attributes with insufficient data"):
  with members:
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "a large scale attribute dataset for zero shot learning"
    - "a modulation module for multi task learning with applications in image retrieval"
    - "pseudo mask augmented object detection"
    - "cbam convolutional block attention module"
    - "non local neural networks"
    - "attribute recognition by joint recurrent learning of context and correlation"
    - "zero shot recognition using dual visual semantic mapping paths"

Connected through hyperedge HE-5 (induced by source paper "score cam improved visual explanations via score weighted class activation mapping"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "interpretable and fine grained visual explanations for convolutional neural networks"
    - "smooth grad cam an enhanced inference level visualization technique for deep convolutional neural network models"
    - "u cam visual explanation using uncertainty based class activation maps"
    - "towards explanation of dnn based prediction with guided feature inversion"
    - "techniques for interpretable machine learning"
    - "local explanation methods for deep neural networks lack sensitivity to parameter values"
    - "explaining image classifiers by counterfactual generation"

Connected through hyperedge HE-6 (induced by source paper "unsupervised deep metric learning via auxiliary rotation loss"):
  with members:
    - "on mutual information maximization for representation learning"
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "hardness aware deep metric learning"
    - "using self supervised learning can improve model robustness and uncertainty"
    - "unsupervised pre training of image features on non curated data"
    - "data efficient image recognition with contrastive predictive coding"
    - "learning representations by maximizing mutual information across views"
    - "unsupervised embedding learning via invariant and spreading instance feature"

Connected through hyperedge HE-7 (induced by source paper "cross domain adaptation for animal pose estimation"):
  with members:
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"
    - "cross domain weakly supervised object detection through progressive domain adaptation"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "3d human pose estimation in the wild by adversarial learning"
    - "partial adversarial domain adaptation"
    - "instagan instance aware image to image translation"
    - "deep adversarial attention alignment for unsupervised domain adaptation the benefit of target expectation maximization"
    - "cross modal deep variational hand pose estimation"

Connected through hyperedge HE-8 (induced by source paper "rgb d individual segmentation"):
  with members:
    - "lf net learning local features from images"
    - "srda generating instance segmentation annotation via scanning reasoning and domain adaptation"
    - "applying domain randomization to synthetic data for object category detection"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "neighbourhood consensus networks"
    - "semi parametric image synthesis"
    - "path aggregation network for instance segmentation"
    - "sniper efficient multi scale training"

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)