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: "summoning demons the pursuit of exploitable bugs in machine learning"
  Abstract: Governments and businesses increasingly rely on data analytics and machine learning (ML) for improving their competitive edge in areas such as consumer satisfaction, threat intelligence, decision making, and product efficiency. However, by cleverly corrupting a subset of data used as input to a target's ML algorithms, an adversary can perturb outcomes and compromise the effectiveness of ML technology. While prior work in the field of adversarial machine learning has studied the impact of input manipulation on correct ML algorithms, we consider the exploitation of bugs in ML implementations. In this paper, we characterize the attack surface of ML programs, and we show that malicious inputs exploiting implementation bugs enable strictly more powerful attacks than the classic adversarial machine learning techniques. We propose a semi-automated technique, called steered fuzzing, for exploring this attack surface and for discovering exploitable bugs in machine learning programs, in order to demonstrate the magnitude of this threat. As a result of our work, we responsibly disclosed five vulnerabilities, established three new CVE-IDs, and illuminated a common insecure practice across many machine learning systems. Finally, we outline several research directions for further understanding and mitigating this threat.

Connected through hyperedge HE-1 (induced by source paper "hacking neural networks a short introduction"):
  with members:
    - "deep leakage from gradients"
    - "iris verification with convolutional neural network and unit circle layer"
    - "adversarial attacks and defences competition"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "backdooring convolutional neural networks via targeted weight perturbations"
    - "invisible mask practical attacks on face recognition with infrared"
    - "microsoft malware classification challenge"
    - "a survey of techniques for improving security of gpus"

Connected through hyperedge HE-2 (induced by source paper "preventing poisoning attacks on ai based threat intelligence systems"):
  with members:
    - "thinking fast and slow combining vector spaces and knowledge graphs"
    - "tweetcred real time credibility assessment of content on twitter"
    - "ontological approach toward cybersecurity in cloud computing"

Connected through hyperedge HE-3 (induced by source paper "security risks in deep learning implementations"):
  with members:
    - "sok security and privacy in machine learning"
    - "practical black box attacks against machine learning"
    - "distillation as a defense to adversarial perturbations against deep neural networks"
    - "the limitations of deep learning in adversarial settings"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"
    - "caffe convolutional architecture for fast feature embedding"
    - "poisoning attacks against support vector machines"

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)