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: "robustness to adversarial examples through an ensemble of specialists"
  Abstract: We are proposing to use an ensemble of diverse specialists, where speciality is defined according to the confusion matrix. Indeed, we observed that for adversarial instances originating from a given class, labeling tend to be done into a small subset of (incorrect) classes. Therefore, we argue that an ensemble of specialists should be better able to identify and reject fooling instances, with a high entropy (i.e., disagreement) over the decisions in the presence of adversaries. Experimental results obtained confirm that interpretation, opening a way to make the system more robust to adversarial examples through a rejection mechanism, rather than trying to classify them properly at any cost.

Connected through hyperedge HE-1 (induced by source paper "g odel s sentence is an adversarial example but unsolvable"):
  with members:
    - "adversarial robustness may be at odds with simplicity"
    - "adversarial examples are a natural consequence of test error in noise"
    - "text processing like humans do visually attacking and shielding nlp systems"
    - "natural adversarial examples"
    - "on the sensitivity of adversarial robustness to input data distributions"
    - "fooling automated surveillance cameras adversarial patches to attack person detection"
    - "probing neural network comprehension of natural language arguments"
    - "adversarial attacks on deep learning models in natural language processing a survey"

Connected through hyperedge HE-2 (induced by source paper "input validation for neural networks via runtime local robustness verification"):
  with members:
    - "hopskipjumpattack a query efficient decision based attack"
    - "outside the box abstraction based monitoring of neural networks"
    - "formal security analysis of neural networks using symbolic intervals"
    - "detecting adversarial samples for deep neural networks through mutation testing"
    - "soter programming safe robotics system using runtime assurance"
    - "soter a runtime assurance framework for programming safe robotics systems"
    - "magnet a two pronged defense against adversarial examples"
    - "safetynet detecting and rejecting adversarial examples robustly"

Connected through hyperedge HE-3 (induced by source paper "lower bounds on adversarial robustness from optimal transport"):
  with members:
    - "adversarial examples are a natural consequence of test error in noise"
    - "vc classes are adversarially robustly learnable but only improperly"
    - "theoretically principled trade off between robustness and accuracy"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "analyzing federated learning through an adversarial lens"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "commandersong a systematic approach for practical adversarial voice recognition"

Connected through hyperedge HE-4 (induced by source paper "n ml mitigating adversarial examples via ensembles of topologically manipulated classifiers"):
  with members:
    - "should adversarial attacks use pixel p norm"
    - "certified adversarial robustness via randomized smoothing"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "adversarial training for free"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "protecting neural networks with hierarchical random switching towards better robustness accuracy trade off for stochastic defenses"
    - "a convex relaxation barrier to tight robustness verification of neural networks"

Connected through hyperedge HE-5 (induced by source paper "defending against adversarial iris examples using wavelet decomposition"):
  with members:
    - "using deep cross modal hashing and error correcting codes for improving the efficiency of attribute guided facial image retrieval"
    - "learning to authenticate with deep multibiometric hashing and neural network decoding"
    - "adversarial examples to fool iris recognition systems"
    - "multi level feature abstraction from convolutional neural networks for multimodal biometric identification"
    - "fast geometrically perturbed adversarial faces"
    - "generalized bilinear deep convolutional neural networks for multimodal biometric identification"
    - "a deep face identification network enhanced by facial attributes prediction"
    - "adversarial examples are not easily detected bypassing ten detection methods"

Connected through hyperedge HE-6 (induced by source paper "preserving differential privacy in adversarial learning with provable robustness"):
  with members:
    - "privacy preserving collaborative deep learning with irregular participants"
    - "sok security and privacy in machine learning"
    - "certified defenses against adversarial examples"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "extending defensive distillation"
    - "blocking transferability of adversarial examples in black box learning systems"
    - "adaptive laplace mechanism differential privacy preservation in deep learning"

Connected through hyperedge HE-7 (induced by source paper "defending against misclassification attacks in transfer learning"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "on evaluating adversarial robustness"
    - "model reuse attacks on deep learning systems"
    - "to compress or not to compress understanding the interactions between adversarial attacks and neural network compression"
    - "why do adversarial attacks transfer explaining transferability of evasion and poisoning attacks"
    - "practical black box attacks against machine learning"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "adversarial machine learning at scale"

Connected through hyperedge HE-8 (induced by source paper "robust attacks against multiple classifiers"):
  with members:
    - "adversarially robust generalization requires more data"
    - "black box adversarial attacks with limited queries and information"
    - "certified defenses against adversarial examples"
    - "certified defenses for data poisoning attacks"
    - "robust optimization for non convex objectives"
    - "decoupling when to update from how to update"
    - "adversarial example defenses ensembles of weak defenses are not strong"
    - "universal adversarial perturbations"

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.NE(Neural and Evolutionary Computing)