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: "deepapt nation state apt attribution using end to end deep neural networks"
  Abstract: In recent years numerous advanced malware, aka advanced persistent threats (APT) are allegedly developed by nation-states. The task of attributing an APT to a specific nation-state is extremely challenging for several reasons. Each nation-state has usually more than a single cyber unit that develops such advanced malware, rendering traditional authorship attribution algorithms useless. Furthermore, those APTs use state-of-the-art evasion techniques, making feature extraction challenging. Finally, the dataset of such available APTs is extremely small.

Connected through hyperedge HE-1 (induced by source paper "stealing knowledge from protected deep neural networks using composite unlabeled data"):
  with members:
    - "copycat cnn stealing knowledge by persuading confession with random non labeled data"
    - "digital watermarking for deep neural networks"
    - "have you stolen my model evasion attacks against deep neural network watermarking techniques"
    - "defending against machine learning model stealing attacks using deceptive perturbations"
    - "deepsigns a generic watermarking framework for ip protection of deep learning models"
    - "embedding watermarks into deep neural networks"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"
    - "adversarial frontier stitching for remote neural network watermarking"

Connected through hyperedge HE-2 (induced by source paper "security for machine learning based systems attacks and challenges during training and inference"):
  with members:
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "deepsafe a data driven approach for checking adversarial robustness in neural networks"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"
    - "cryptodl deep neural networks over encrypted data"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "is deep learning safe for robot vision adversarial examples against the icub humanoid"
    - "safetynets verifiable execution of deep neural networks on an untrusted cloud"
    - "modular learning component attacks today s reality tomorrow s challenge"

Connected through hyperedge HE-3 (induced by source paper "end to end deep neural networks and transfer learning for automatic analysis of nation state malware"):
  with members:
    - "when coding style survives compilation de anonymizing programmers from executable binaries"
    - "on the feasibility of malware authorship attribution"
    - "deepsign deep learning for automatic malware signature generation and classification"
    - "novel feature extraction selection and fusion for effective malware family classification"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "visualizing and understanding convolutional networks"
    - "representation learning a review and new perspectives"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-4 (induced by source paper "generic black box end to end attack against state of the art api call based malware classifiers"):
  with members:
    - "sok security and privacy in machine learning"
    - "black box attacks against rnn based malware detection algorithms"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "generating adversarial malware examples for black box attacks based on gan"
    - "on the statistical detection of adversarial examples"
    - "adversarial perturbations against deep neural networks for malware classification"
    - "crafting adversarial input sequences for recurrent neural networks"
    - "practical black box attacks against machine learning"

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)