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: "stateful detection of black box adversarial attacks"
  Abstract: The problem of adversarial examples, evasion attacks on machine learning classifiers, has proven extremely difficult to solve. This is true even when, as is the case in many practical settings, the classifier is hosted as a remote service and so the adversary does not have direct access to the model parameters. #R##N#This paper argues that in such settings, defenders have a much larger space of actions than have been previously explored. Specifically, we deviate from the implicit assumption made by prior work that a defense must be a stateless function that operates on individual examples, and explore the possibility for stateful defenses. #R##N#To begin, we develop a defense designed to detect the process of adversarial example generation. By keeping a history of the past queries, a defender can try to identify when a sequence of queries appears to be for the purpose of generating an adversarial example. We then introduce query blinding, a new class of attacks designed to bypass defenses that rely on such a defense approach. #R##N#We believe that expanding the study of adversarial examples from stateless classifiers to stateful systems is not only more realistic for many black-box settings, but also gives the defender a much-needed advantage in responding to the adversary.

Connected through hyperedge HE-1 (induced by source paper "towards certifiable adversarial sample detection"):
  with members:
    - "adversarial defense by restricting the hidden space of deep neural networks"
    - "certified adversarial robustness via randomized smoothing"
    - "gotta catch em all using concealed trapdoors to detect adversarial attacks on neural networks"
    - "sitatapatra blocking the transfer of adversarial samples"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "scaling provable adversarial defenses"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "to compress or not to compress understanding the interactions between adversarial attacks and neural network compression"

Connected through hyperedge HE-2 (induced by source paper "geoda a geometric framework for black box adversarial attacks"):
  with members:
    - "a geometry inspired decision based attack"
    - "hopskipjumpattack a query efficient decision based attack"
    - "simple black box adversarial attacks"
    - "improving black box adversarial attacks with a transfer based prior"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "black box adversarial attacks with limited queries and information"
    - "query efficient hard label black box attack an optimization based approach"
    - "simple black box adversarial perturbations for deep networks"

Connected through hyperedge HE-3 (induced by source paper "adversarial perceptual ad blocking meets adversarial machine learning"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "percival making in browser perceptual ad blocking practical with deep learning"
    - "who filters the filters understanding the growth usefulness and efficiency of crowdsourced ad blocking"
    - "sentinet detecting physical attacks against deep learning systems"
    - "yolov3 an incremental improvement"
    - "fooling ocr systems with adversarial text images"
    - "adversarially robust generalization requires more data"
    - "black box adversarial attacks with limited queries and information"

Connected through hyperedge HE-4 (induced by source paper "blackbox attacks on reinforcement learning agents using approximated temporal information"):
  with members:
    - "sitatapatra blocking the transfer of adversarial samples"
    - "an algorithmic perspective on imitation learning"
    - "black box adversarial attacks with limited queries and information"
    - "behavioral cloning from observation"
    - "the taboo trap behavioural detection of adversarial samples"
    - "rainbow combining improvements in deep reinforcement learning"
    - "adversarial attacks on neural network policies"
    - "tactics of adversarial attack on deep reinforcement learning agents"

Connected through hyperedge HE-5 (induced by source paper "hybrid batch attacks finding black box adversarial examples with limited queries"):
  with members:
    - "hopskipjumpattack a query efficient decision based attack"
    - "there are no bit parts for sign bits in black box attacks"
    - "simple black box adversarial attacks"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "improving black box adversarial attacks with a transfer based prior"
    - "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "targeted adversarial examples for black box audio systems"

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.CR(Cryptography and Security)