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: "software defined networking based crypto ransomware detection using http traffic characteristics"
  Abstract: Abstract   Ransomware is currently one of the key threats facing individuals and corporate Internet users. Especially dangerous is crypto ransomware that encrypts important user data, and it is only possible to recover it once a ransom has been paid. Therefore, devising efficient and effective countermeasures is a pressing necessity. In this paper we present a novel Software-Defined Networking (SDN) based detection approach that utilizes the characteristics of the ransomware communication. Based on an observation of network communication between two crypto ransomware families, namely CryptoWall and Locky, we conclude that an analysis of the HTTP message sequences and their respective content sizes is enough to detect such threats. We show the feasibility of our approach by designing and evaluating a proof-of-concept SDN-based detection system. The experimental results confirm that the proposed approach is feasible and efficient.

Connected through hyperedge HE-1 (induced by source paper "volenti non fit injuria ransomware and its victims"):
  with members:
    - "temporal graph offset reconstruction towards temporally robust graph representation learning"
    - "on the economics of ransomware"
    - "exploit kits the production line of the cybercrime economy"
    - "node2vec scalable feature learning for networks"
    - "automated dynamic analysis of ransomware benefits limitations and use for detection"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "ransomware analysis using feature engineering and deep neural networks"):
  with members:
    - "a recent survey on the applications of genetic programming in image processing"
    - "a survey of the recent architectures of deep convolutional neural networks"
    - "a new channel boosted convolution neural network using transfer learning"
    - "automated behavioral analysis of malware a case study of wannacry ransomware"
    - "automated dynamic analysis of ransomware benefits limitations and use for detection"
    - "deep neural network based malware detection using two dimensional binary program features"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "towards resilient machine learning for ransomware detection"):
  with members:
    - "deep learning for malicious flow detection"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "detecting adversarial examples via neural fingerprinting"
    - "leveraging machine learning techniques for windows ransomware network traffic detection"
    - "adversarial logit pairing"
    - "deep transfer learning for static malware classification"
    - "generating adversarial examples with adversarial networks"
    - "black box adversarial attacks with limited queries and information"

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)