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: "detection of malicious and low throughput data exfiltration over the dns protocol"
  Abstract: In the presence of security countermeasures, a malware designed for data exfiltration must do so using a covert channel to achieve its goal. Among existing covert channels stands the domain name system (DNS) protocol. Although the detection of covert channels over the DNS has been thoroughly studied in the last decade, previous research dealt with a specific subclass of covert channels, namely DNS tunneling. While the importance of tunneling detection is not undermined, an entire class of low throughput DNS exfiltration malware remained overlooked. The goal of this study is to propose a method for detecting both tunneling and low-throughput data exfiltration over the DNS. Towards this end, we propose a solution composed of a supervised feature selection method, and an interchangeable, and adjustable anomaly detection model trained on legitimate traffic. In the first step, a one-class classifier is applied for detecting domain-specific traffic that does not conform with the normal behavior. Then, in the second step, in order to reduce the false positive rate resulting from the attempt to detect the low-throughput data exfiltration we apply a rule-based filter that filters data exchange over DNS used by legitimate services. Our solution was evaluated on a medium-scale recursive DNS server logs, and involved more than 75,000 legitimate uses and almost 2,000 attacks. Evaluation results shows that while DNS tunneling is covered with at least 99% recall rate and less than 0.01% false positive rate, the detection of low throughput exfiltration is more difficult. While not preventing it completely, our solution limits a malware attempting to avoid detection with at most a 1kb/h of payload under the limitations of the DNS syntax (equivalent to five credit cards details, or ten user credentials per hour) which reduces the effectiveness of the attack.

Connected through hyperedge HE-1 (induced by source paper "maskdga a black box evasion technique against dga classifiers and adversarial defenses"):
  with members:
    - "detecting dga domains with recurrent neural networks and side information"
    - "inline detection of domain generation algorithms with context sensitive word embeddings"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "exploring adversarial examples in malware detection"
    - "expose a character level convolutional neural network with embeddings for detecting malicious urls file paths and registry keys"
    - "hotflip white box adversarial examples for text classification"
    - "generating adversarial malware examples for black box attacks based on gan"
    - "generic black box end to end attack against state of the art api call based malware classifiers"

Connected through hyperedge HE-2 (induced by source paper "identifying dns tunneled traffic with predictive models"):
  with members:
    - "entropy based prediction of network protocols in the forensic analysis of dns tunnels"
    - "detecting dns tunnels using character frequency analysis"

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)