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: "maximum entropy fine grained classification"
  Abstract: Fine-Grained Visual Classification (FGVC) is an important computer vision problem that involves small diversity within the different classes, and often requires expert annotators to collect data. Utilizing this notion of small visual diversity, we revisit Maximum-Entropy learning in the context of fine-grained classification, and provide a training routine that maximizes the entropy of the output probability distribution for training convolutional neural networks on FGVC tasks. We provide a theoretical as well as empirical justification of our approach, and achieve state-of-the-art performance across a variety of classification tasks in FGVC, that can potentially be extended to any fine-tuning task. Our method is robust to different hyperparameter values, amount of training data and amount of training label noise and can hence be a valuable tool in many similar problems.

Connected through hyperedge HE-1 (induced by source paper "unsupervised domain adaptive object detection using forward backward cyclic adaptation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "beyond sharing weights for deep domain adaptation"
    - "on first order meta learning algorithms"
    - "semantic foggy scene understanding with synthetic data"
    - "reptile a scalable metalearning algorithm"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "co regularized alignment for unsupervised domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "spatial scale aligned network for fine grained recognition"):
  with members:
    - "looking for the devil in the details learning trilinear attention sampling network for fine grained image recognition"
    - "learning to navigate for fine grained classification"
    - "towards faster training of global covariance pooling networks by iterative matrix square root normalization"
    - "feature pyramid networks for object detection"
    - "learning a discriminative filter bank within a cnn for fine grained recognition"
    - "discriminative scale space tracking"
    - "compact bilinear pooling"
    - "bilinear cnn models for fine grained visual recognition"

Connected through hyperedge HE-3 (induced by source paper "bag of tricks for retail product image classification"):
  with members:
    - "benchmark for generic product detection a strong baseline for dense object detection"
    - "robustness properties of facebook s resnext wsl models"
    - "domain invariant hierarchical embedding for grocery products recognition"
    - "pytorch an imperative style high performance deep learning library"
    - "exploring the limits of weakly supervised pretraining"
    - "superpoint self supervised interest point detection and description"
    - "product recognition in store shelves as a sub graph isomorphism problem"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "attention convolutional binary neural tree for fine grained visual categorization"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "see better before looking closer weakly supervised data augmentation network for fine grained visual classification"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "looking for the devil in the details learning trilinear attention sampling network for fine grained image recognition"
    - "knowledge embedded representation learning for fine grained image recognition"
    - "fine grained classification of cervical cells using morphological and appearance based convolutional neural networks"
    - "adaptive neural trees"
    - "learn to pay attention"

Connected through hyperedge HE-5 (induced by source paper "cross x learning for fine grained visual categorization"):
  with members:
    - "learning to navigate for fine grained classification"
    - "sampling matters in deep embedding learning"
    - "deep layer aggregation"
    - "dynamic computational time for visual attention"
    - "fully convolutional attention localization networks efficient attention localization for fine grained recognition"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "ssd single shot multibox detector"
    - "localizing by describing attribute guided attention localization for fine grained recognition"

Connected through hyperedge HE-6 (induced by source paper "fine grained recognition accounting for subtle differences between similar classes"):
  with members:
    - "looking for the devil in the details learning trilinear attention sampling network for fine grained image recognition"
    - "weakly supervised complementary parts models for fine grained image classification from the bottom up"
    - "multi attention multi class constraint for fine grained image recognition"
    - "deep texture manifold for ground terrain recognition"
    - "learning to navigate for fine grained classification"
    - "rethinking feature distribution for loss functions in image classification"
    - "pairwise confusion for fine grained visual classification"
    - "towards faster training of global covariance pooling networks by iterative matrix square root normalization"

Connected through hyperedge HE-7 (induced by source paper "semi supervised domain adaptation via minimax entropy"):
  with members:
    - "a closer look at few shot classification"
    - "moment matching for multi source domain adaptation"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "dynamic few shot visual learning without forgetting"
    - "strong weak distribution alignment for adaptive object detection"
    - "deep hashing network for unsupervised domain adaptation"
    - "maximum classifier discrepancy for unsupervised domain adaptation"
    - "good semi supervised learning that requires a bad gan"

Connected through hyperedge HE-8 (induced by source paper "deep learning for fine grained image analysis a survey"):
  with members:
    - "piecewise classifier mappings learning fine grained learners for novel categories with few examples"
    - "rpc a large scale retail product checkout dataset"
    - "fine grained visual categorization using meta learning optimization with sample selection of auxiliary data"
    - "multi attention multi class constraint for fine grained image recognition"
    - "learning to navigate for fine grained classification"
    - "coarse to fine a rnn based hierarchical attention model for vehicle re identification"
    - "part aligned bilinear representations for person re identification"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"

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)