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 "early stage malware prediction using recurrent neural networks"):
  Abstract: Static malware analysis is well-suited to endpoint anti-virus systems as it can be conducted quickly by examining the features of an executable piece of code and matching it to previously observed malicious code. However, static code analysis can be vulnerable to code obfuscation techniques. Behavioural data collected during file execution is more difficult to obfuscate, but takes a relatively long time to capture - typically up to 5 minutes, meaning the malicious payload has likely already been delivered by the time it is detected. In this paper we investigate the possibility of predicting whether or not an executable is malicious based on a short snapshot of behavioural data. We find that an ensemble of recurrent neural networks are able to predict whether an executable is malicious or benign within the first 5 seconds of execution with 94% accuracy. This is the first time general types of malicious file have been predicted to be malicious during execution rather than using a complete activity log file post-execution, and enables cyber security endpoint protection to be advanced to use behavioural data for blocking malicious payloads rather than detecting them post-execution and having to repair the damage.

Includes members:
  Member "lstm a search space odyssey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units")
  Member "virtual machine introspection based malware behavior profiling and family grouping":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "integration of static and dynamic analysis for malware family classification with composite neural network")
  Member "generic black box end to end attack against state of the art api call based malware classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "automatic generation of adversarial examples for interpreting malware classifiers")
  Member "adversarial perturbations against deep neural networks for malware classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the security of randomized defenses against adversarial samples")
  Member "a critical review of recurrent neural networks for sequence learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ten social dimensions of conversations and relationships")
  Member "tensorflow large scale machine learning on heterogeneous distributed systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "deep neural network based malware detection using two dimensional binary program features":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "automatic generation of adversarial examples for interpreting malware classifiers")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)