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: "malicious behavior detection using windows audit logs"
  Abstract: As antivirus and network intrusion detection systems have increasingly proven insufficient to detect advanced threats, large security operations centers have moved to deploy endpoint-based sensors that provide deeper visibility into low-level events across their enterprises. Unfortunately, for many organizations in government and industry, the installation, maintenance, and resource requirements of these newer solutions pose barriers to adoption and are perceived as risks to organizations' missions. #R##N#To mitigate this problem we investigated the utility of agentless detection of malicious endpoint behavior, using only the standard build-in Windows audit logging facility as our signal. We found that Windows audit logs, while emitting manageable sized data streams on the endpoints, provide enough information to allow robust detection of malicious behavior. Audit logs provide an effective, low-cost alternative to deploying additional expensive agent-based breach detection systems in many government and industrial settings, and can be used to detect, in our tests, 83% percent of malware samples with a 0.1% false positive rate. They can also supplement already existing host signature-based antivirus solutions, like Kaspersky, Symantec, and McAfee, detecting, in our testing environment, 78% of malware missed by those antivirus systems.

Connected through hyperedge HE-1 (induced by source paper "dynamic malware analysis with feature engineering and feature learning"):
  with members:
    - "anmm ranking short answer texts with attention based neural matching model"
    - "session aware information embedding for e commerce product recommendation"
    - "language modeling with gated convolutional networks"
    - "deepsign deep learning for automatic malware signature generation and classification"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "feature hashing for large scale multitask learning"

Connected through hyperedge HE-2 (induced by source paper "computer systems have 99 problems let s not make machine learning another one"):
  with members:
    - "atmpa attacking machine learning based malware visualization detection methods via adversarial examples"
    - "a context aware framework for detecting sensor based threats on smart devices"
    - "copycat practical adversarial attacks on visualization based malware detection"
    - "robust physical world attacks on deep learning models"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "a survey on deep packet inspection for intrusion detection systems"

Connected through hyperedge HE-3 (induced by source paper "holmes real time apt detection through correlation of suspicious information flows"):
  with members:
    - "runtime analysis of whole system provenance"
    - "sleuth real time attack scenario reconstruction from cots audit data"

Connected through hyperedge HE-4 (induced by source paper "network based analysis and classification of malware using behavioral artifacts ordering"):
  with members:
    - "mamadroid detecting android malware by building markov chains of behavioral models"
    - "malware classification based on call graph clustering"

Connected through hyperedge HE-5 (induced by source paper "expose a character level convolutional neural network with embeddings for detecting malicious urls file paths and registry keys"):
  with members:
    - "identity mappings in deep residual networks"
    - "neural machine translation in linear time"
    - "multi scale context aggregation by dilated convolutions"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"

Connected through hyperedge HE-6 (induced by source paper "learning adversary resistant deep neural networks"):
  with members:
    - "adversarial perturbations against deep neural networks for malware classification"
    - "delving into transferable adversarial examples and black box attacks"
    - "measuring neural net robustness with constraints"
    - "unifying adversarial training algorithms with flexible deep data gradient regularization"
    - "on the effectiveness of defensive distillation"
    - "towards evaluating the robustness of neural networks"
    - "distillation as a defense to adversarial perturbations against deep neural networks"
    - "adversarial manipulation of deep representations"

Connected through hyperedge HE-7 (induced by source paper "adversary resistant deep neural networks with an application to malware detection"):
  with members:
    - "adversarial perturbations against deep neural networks for malware classification"
    - "unifying adversarial training algorithms with flexible deep data gradient regularization"
    - "defensive distillation is not robust to adversarial examples"
    - "deepdga adversarially tuned domain generation and detection"
    - "deep neural network based malware detection using two dimensional binary program features"
    - "distillation as a defense to adversarial perturbations against deep neural networks"
    - "towards deep neural network architectures robust to adversarial examples"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-8 (induced by source paper "deep neural network based malware detection using two dimensional binary program features"):
  with members:
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "feature hashing for large scale multitask 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)