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 towards minimum hyperspherical energy"
  Abstract: Neural networks are a powerful class of nonlinear functions that can be trained end-to-end on various applications. While the over-parametrization nature in many neural networks renders the ability to fit complex functions and the strong representation power to handle challenging tasks, it also leads to highly correlated neurons that can hurt the generalization ability and incur unnecessary computation cost. As a result, how to regularize the network to avoid undesired representation redundancy becomes an important issue. To this end, we draw inspiration from a well-known problem in physics -- Thomson problem, where one seeks to find a state that distributes N electrons on a unit sphere as evenly as possible with minimum potential energy. In light of this intuition, we reduce the redundancy regularization problem to generic energy minimization, and propose a minimum hyperspherical energy (MHE) objective as generic regularization for neural networks. We also propose a few novel variants of MHE, and provide some insights from a theoretical point of view. Finally, we apply neural networks with MHE regularization to several challenging tasks. Extensive experiments demonstrate the effectiveness of our intuition, by showing the superior performance with MHE regularization.

Connected through hyperedge HE-1 (induced by source paper "vec2face unveil human faces from their blackbox features in face recognition"):
  with members:
    - "black box adversarial attacks with bayesian optimization"
    - "shrinkteanet million scale lightweight face recognition via shrinking teacher student networks"
    - "adacos adaptively scaling cosine logits for effectively learning deep face representations"
    - "fooling automated surveillance cameras adversarial patches to attack person detection"
    - "arcface additive angular margin loss for deep face recognition"
    - "black box adversarial attacks with limited queries and information"
    - "constructing unrestricted adversarial examples with generative models"
    - "mobiface a lightweight deep learning face recognition on mobile devices"

Connected through hyperedge HE-2 (induced by source paper "neural similarity learning"):
  with members:
    - "compressive hyperspherical energy minimization"
    - "implicit regularization in deep matrix factorization"
    - "a closer look at few shot classification"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "decoupled networks"
    - "meta learning with latent embedding optimization"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "towards understanding the role of over parametrization in generalization of neural networks"

Connected through hyperedge HE-3 (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-4 (induced by source paper "improving neural language modeling via adversarial training"):
  with members:
    - "partially shuffling the training data to improve language models"
    - "representation degeneration problem in training natural language generation models"
    - "fast parametric learning with activation memorization"
    - "frage frequency agnostic word representation"
    - "a la carte embedding cheap but effective induction of semantic feature vectors"
    - "an analysis of neural language modeling at multiple scales"
    - "darts differentiable architecture search"
    - "additive margin softmax for face verification"

Connected through hyperedge HE-5 (induced by source paper "instance cross entropy for deep metric learning"):
  with members:
    - "ranked list loss for deep metric learning"
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "hardness aware deep metric learning"
    - "signal to noise ratio a robust distance metric for deep metric learning"
    - "divide and conquer the embedding space for metric learning"
    - "deep metric learning beyond binary supervision"
    - "decoupled networks"
    - "deep randomized ensembles for metric learning"

Connected through hyperedge HE-6 (induced by source paper "angular visual hardness"):
  with members:
    - "feature critic networks for heterogeneous domain generalization"
    - "confidence regularized self training"
    - "compressive hyperspherical energy minimization"
    - "do imagenet classifiers generalize to imagenet"
    - "decoupled networks"
    - "reducing overconfident errors outside the known distribution"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "not all samples are created equal deep learning with importance sampling"

Connected through hyperedge HE-7 (induced by source paper "pseudo labeling and confirmation bias in deep semi supervised learning"):
  with members:
    - "exploiting unlabeled data in cnns by self supervised learning to rank"
    - "surprising effectiveness of few image unsupervised feature learning"
    - "unsupervised label noise modeling and loss correction"
    - "semi supervised learning with scarce annotations"
    - "label propagation for deep semi supervised learning"
    - "mixmatch a holistic approach to semi supervised learning"
    - "revisiting self supervised visual representation learning"
    - "there are many consistent explanations of unlabeled data why you should average"

Connected through hyperedge HE-8 (induced by source paper "shrinkteanet million scale lightweight face recognition via shrinking teacher student networks"):
  with members:
    - "fishnet a versatile backbone for image region and pixel level prediction"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "knowledge transfer with jacobian matching"
    - "paraphrasing complex network network compression via factor transfer"
    - "arcface additive angular margin loss for deep face recognition"
    - "multicolumn networks for face recognition"
    - "mobilefacenets efficient cnns for accurate real time face verification on mobile devices"
    - "knowledge transfer via distillation of activation boundaries formed by hidden neurons"

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.LG(Machine Learning)