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: "adversarial initialization when your network performs the way i want"
  Abstract: The increase in computational power and available data has fueled a wide deployment of deep learning in production environments. Despite their successes, deep architectures are still poorly understood and costly to train. We demonstrate in this paper how a simple recipe enables a market player to harm or delay the development of a competing product. Such a threat model is novel and has not been considered so far. We derive the corresponding attacks and show their efficacy both formally and empirically. These attacks only require access to the initial, untrained weights of a network. No knowledge of the problem domain and the data used by the victim is needed. On the initial weights, a mere permutation is sufficient to limit the achieved accuracy to for example 50% on the MNIST dataset or double the needed training time. While we can show straightforward ways to mitigate the attacks, the respective steps are not part of the standard procedure taken by developers so far.

Connected through hyperedge HE-1 (induced by source paper "salvaging federated learning by local adaptation"):
  with members:
    - "differential privacy has disparate impact on model accuracy"
    - "advances and open problems in federated learning"
    - "one shot federated learning"
    - "improving federated learning personalization via model agnostic meta learning"
    - "towards federated learning at scale system design"
    - "detox a redundancy based framework for faster and more robust gradient aggregation"
    - "federated evaluation of on device personalization"
    - "measuring the effects of non identical data distribution for federated visual classification"

Connected through hyperedge HE-2 (induced by source paper "correlated initialization for correlated data"):
  with members:
    - "fixup initialization residual learning without normalization"
    - "locality promoting representation learning"
    - "acnet strengthening the kernel skeletons for powerful cnn via asymmetric convolution blocks"
    - "rethinking imagenet pre training"
    - "towards understanding regularization in batch normalization"
    - "mean field residual networks on the edge of chaos"
    - "the shattered gradients problem if resnets are the answer then what is the question"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-3 (induced by source paper "universal adversarial audio perturbations"):
  with members:
    - "robust audio adversarial example for a physical attack"
    - "end to end environmental sound classification using a 1d convolutional neural network"
    - "universal adversarial perturbations for speech recognition systems"
    - "sirenattack generating adversarial audio for end to end acoustic systems"
    - "unsupervised cross modal alignment of speech and text embedding spaces"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "unsupervised text style transfer using language models as discriminators"
    - "knockoff nets stealing functionality of black box models"

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)