human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "distillation for run time malware process detection and automated process killing"):
  Abstract: Adversaries are increasingly motivated to spend energy trying to evade automatic malware detection tools. Dynamic analysis examines the behavioural trace of malware, which is difficult to obfuscate, but the time required for dynamic analysis means it is not typically used in practice for endpoint protection but rather as an analysis tool. This paper presents a run-time model to detect malicious processes and automatically kill them as they run on a real endpoint in use. This approach enables dynamic analysis to be used to prevent harm to the endpoint, rather than to analyse the cause of damage after the event. Run-time detection introduces the risk of malicious damage to the endpoint and necessitates that malicious processes are detected and killed as early as possible to minimise the opportunities for damage to take place. A distilled machine learning model is used to improve inference speed whilst benefiting from the parameters learned by larger, more computationally intensive model. This paper is the first to focus on tangible benefits of process killing to the user, showing that the distilled model is able to prevent 86.34% of files being corrupted by ransomware whilst maintaining a low false positive rate for unseen benignware of 4.72%.

Includes members:
  Member "adversarial malware binaries evading deep learning for malware detection in executables":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "automatic generation of adversarial examples for interpreting malware classifiers")
  Member "early stage malware prediction using recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "intrusion detection for industrial control systems evaluation analysis and adversarial attacks")
  Member "learning fast and slow propedeutica for real time malware detection":
    also connected through other hyperedges:
      (none)
  Member "adversarial perturbations against deep neural networks for malware classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the security of randomized defenses against adversarial samples")
  Member "distillation as a defense to adversarial perturbations against deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adversarial ranking attack and defense")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "empirical evaluation of gated recurrent neural networks on sequence modeling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "learning phrase representations using rnn encoder decoder for statistical machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "transformation based adversarial video prediction on large scale data")

Question: Please tell me which class the source paper 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)