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: "catching worms trojan horses and pups unsupervised detection of silent delivery campaigns"
  Abstract: The growing commoditization of the underground economy has given rise to malware delivery networks, which charge fees for quickly delivering malware or unwanted software to a large number of hosts. To provide this service, a key method is the orchestration of silent delivery campaigns, which involve a group of downloaders that receive remote commands and that deliver their payloads without any user interaction. These campaigns have not been characterized systematically, unlike other aspects of malware delivery networks. Moreover, silent delivery campaigns can evade detection by relying on inconspicuous downloaders on the client side and on disposable domain names on the server side. We describe Beewolf, a system for detecting silent delivery campaigns from Internet-wide records of download events. The key observation behind our system is that the downloaders involved in these campaigns frequently retrieve payloads in lockstep. Beewolf identifies such locksteps in an unsupervised and deterministic manner. By exploiting novel techniques and empirical observations, Beewolf can operate on streaming data. We utilize Beewolf to study silent delivery campaigns at scale, on a data set of 33.3 million download events. This investigation yields novel findings, e.g. malware distributed through compromised software update channels, a substantial overlap between the delivery ecosystems for malware and unwanted software, and several types of business relationships within these ecosystems. Beewolf achieves over 92% true positives and fewer than 5% false positives. Moreover, Beewolf can detect suspicious downloaders a median of 165 days ahead of existing anti-virus products and payload-hosting domains a median of 196 days ahead of existing blacklists.

Connected through hyperedge HE-1 (induced by source paper "attack2vec leveraging temporal word embeddings to understand the evolution of cyberattacks"):
  with members:
    - "holmes real time apt detection through correlation of suspicious information flows"
    - "tiresias predicting security events through deep learning"
    - "dynamic word embeddings for evolving semantic discovery"
    - "neural network based graph embedding for cross platform binary code similarity detection"
    - "tweet2vec character based distributed representations for social media"
    - "data poisoning attacks on factorization based collaborative filtering"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "on preempting advanced persistent threats using probabilistic graphical models"):
  with members:
    - "vulnerable open source dependencies counting those that matter"
    - "on the inability of markov models to capture criticality in human mobility"
    - "snorkel rapid training data creation with weak supervision"
    - "thou shalt not depend on me analysing the use of outdated javascript libraries on the web"
    - "attack analysis results for adversarial engagement 1 of the darpa transparent computing program"
    - "towards detecting compromised accounts on social networks"
    - "opengm a c library for discrete graphical models"

Connected through hyperedge HE-3 (induced by source paper "the shape of alerts detecting malware using distributed detectors by robustly amplifying transient correlations"):
  with members:
    - "practical black box attacks against machine learning"
    - "xgboost a scalable tree boosting system"
    - "reviewer integration and performance measurement for malware detection"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-4 (induced by source paper "issued for abuse measuring the underground trade in code signing certificate"):
  with members:
    - "traveling the silk road a measurement analysis of a large anonymous online marketplace"

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)