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: "rethinking feature distribution for loss functions in image classification"
  Abstract: We propose a large-margin Gaussian Mixture (L-GM) loss for deep neural networks in classification tasks. Different from the softmax cross-entropy loss, our proposal is established on the assumption that the deep features of the training set follow a Gaussian Mixture distribution. By involving a classification margin and a likelihood regularization, the L-GM loss facilitates both a high classification performance and an accurate modeling of the training feature distribution. As such, the L-GM loss is superior to the softmax loss and its major variants in the sense that besides classification, it can be readily used to distinguish abnormal inputs, such as the adversarial examples, based on their features' likelihood to the training feature distribution. Extensive experiments on various recognition benchmarks like MNIST, CIFAR, ImageNet and LFW, as well as on adversarial examples demonstrate the effectiveness of our proposal.

Connected through hyperedge HE-1 (induced by source paper "the devil is in the channels mutual channel loss for fine grained image classification"):
  with members:
    - "deep learning for free hand sketch a survey"
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "multi graph transformer for free hand sketch recognition"
    - "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"
    - "object part attention model for fine grained image classification"
    - "cosface large margin cosine loss for deep face recognition"
    - "learning to navigate for fine grained classification"

Connected through hyperedge HE-2 (induced by source paper "rethinking softmax cross entropy loss for adversarial robustness"):
  with members:
    - "adversarial defense by restricting the hidden space of deep neural networks"
    - "adversarial examples are a natural consequence of test error in noise"
    - "are labels required for improving adversarial robustness"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial training for free"
    - "on evaluating adversarial robustness"
    - "scaling provable adversarial defenses"

Connected through hyperedge HE-3 (induced by source paper "dense classification and implanting for few shot learning"):
  with members:
    - "fix your classifier the marginal value of training the last weight layer"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "ring loss convex feature normalization for face recognition"
    - "path aggregation network for instance segmentation"
    - "low shot learning from imaginary data"
    - "dynamic few shot visual learning without forgetting"
    - "packnet adding multiple tasks to a single network by iterative pruning"

Connected through hyperedge HE-4 (induced by source paper "metaadvdet towards robust detection of evolving adversarial attacks"):
  with members:
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks"
    - "spatially transformed adversarial examples"
    - "detecting adversarial examples via neural fingerprinting"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "adversarially learned one class classifier for novelty detection"
    - "darccc detecting adversaries by reconstruction from class conditional capsules"

Connected through hyperedge HE-5 (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"
    - "maximum entropy fine grained classification"
    - "learning to navigate for fine grained 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-6 (induced by source paper "variational metric scaling for metric based meta learning"):
  with members:
    - "finding task relevant features for few shot learning by category traversal"
    - "on first order meta learning algorithms"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "few shot object detection via feature reweighting"
    - "gaussian prototypical networks for few shot learning on omniglot"
    - "probabilistic model agnostic meta learning"
    - "large margin few shot learning"
    - "heated up softmax embedding"

Connected through hyperedge HE-7 (induced by source paper "deep manifold embedding for hyperspectral image classification"):
  with members:
    - "diversity in machine learning"
    - "geometric understanding of deep learning"
    - "mining on manifolds metric learning without labels"
    - "image reconstruction by domain transform manifold learning"
    - "deep class wise hashing semantics preserving hashing via class wise loss"
    - "going deeper with contextual cnn for hyperspectral image classification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "deep metric learning with angular loss"

Connected through hyperedge HE-8 (induced by source paper "large margin softmax loss for speaker verification"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "learning towards minimum hyperspherical energy"
    - "ring loss convex feature normalization for face recognition"
    - "voxceleb2 deep speaker recognition"
    - "additive margin softmax for face verification"
    - "how to improve your speaker embeddings extractor in generic toolkits"
    - "sampling matters in deep embedding learning"

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)