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: "max mahalanobis linear discriminant analysis networks"
  Abstract: A deep neural network (DNN) consists of a nonlinear transformation from an input to a feature representation, followed by a common softmax linear classifier. Though many efforts have been devoted to designing a proper architecture for nonlinear transformation, little investigation has been done on the classifier part. In this paper, we show that a properly designed classifier can improve robustness to adversarial attacks and lead to better prediction results. Specifically, we define a Max-Mahalanobis distribution (MMD) and theoretically show that if the input distributes as a MMD, the linear discriminant analysis (LDA) classifier will have the best robustness to adversarial examples. We further propose a novel Max-Mahalanobis linear discriminant analysis (MM-LDA) network, which explicitly maps a complicated data distribution in the input space to a MMD in the latent feature space and then applies LDA to make predictions. Our results demonstrate that the MM-LDA networks are significantly more robust to adversarial attacks, and have better performance in class-biased classification.

Connected through hyperedge HE-1 (induced by source paper "on adaptive attacks to adversarial example defenses"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "adversarial example detection and classification with asymmetrical adversarial training"
    - "an alternative surrogate loss for pgd based adversarial testing"
    - "mixup inference better exploiting mixup to defend adversarial attacks"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "a new defense against adversarial images turning a weakness into a strength"
    - "resisting adversarial attacks by k winners take all"
    - "rethinking softmax cross entropy loss for adversarial robustness"

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 "cluster alignment with a teacher for unsupervised domain adaptation"):
  with members:
    - "domain adaptation with asymmetrically relaxed distribution alignment"
    - "on learning invariant representation for domain adaptation"
    - "image to image translation for domain adaptation"
    - "cycada cycle consistent adversarial domain adaptation"
    - "structured generative adversarial networks"
    - "maximum classifier discrepancy for unsupervised domain adaptation"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "unsupervised domain adaptation with similarity learning"

Connected through hyperedge HE-4 (induced by source paper "mixup inference better exploiting mixup to defend adversarial attacks"):
  with members:
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial training for free"
    - "mixmatch a holistic approach to semi supervised learning"
    - "on evaluating adversarial robustness"
    - "data interpolating prediction alternative interpretation of mixup"
    - "is bert really robust a strong baseline for natural language attack on text classification and entailment"
    - "adversarial attacks and defences competition"
    - "adversarial attack on graph structured data"

Connected through hyperedge HE-5 (induced by source paper "evading defenses to transferable adversarial examples by translation invariant attacks"):
  with members:
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "quantifying translation invariance in convolutional neural networks"
    - "improving transferability of adversarial examples with input diversity"
    - "certified defenses against adversarial examples"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "defense against adversarial attacks using high level representation guided denoiser"
    - "towards robust detection of adversarial examples"

Connected through hyperedge HE-6 (induced by source paper "deep neural rejection against adversarial examples"):
  with members:
    - "detecting adversarial examples through nonlinear dimensionality reduction"
    - "selectivenet a deep neural network with an integrated reject option"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "explaining black box android malware detection"
    - "is feature selection secure against training data poisoning"
    - "why do adversarial attacks transfer explaining transferability of evasion and poisoning attacks"

Connected through hyperedge HE-7 (induced by source paper "improving adversarial robustness via promoting ensemble diversity"):
  with members:
    - "adversarial attacks and defences competition"
    - "adversarial logit pairing"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "on calibration of modern neural networks"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "defense against adversarial attacks using high level representation guided denoiser"
    - "towards robust detection of adversarial examples"
    - "practical black box attacks against machine learning"

Connected through hyperedge HE-8 (induced by source paper "identifying and resisting adversarial videos using temporal consistency"):
  with members:
    - "adversarial framing for image and video classification"
    - "comdefend an efficient image compression model to defend adversarial examples"
    - "sparse adversarial perturbations for videos"
    - "adversarial perturbations against real time video classification systems"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "defense against adversarial attacks using high level representation guided denoiser"
    - "countering adversarial images using input transformations"
    - "mitigating adversarial effects through randomization"

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)