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: "detecting dga domains with recurrent neural networks and side information"
  Abstract: Modern malware typically makes use of a domain generation algorithm (DGA) to avoid command and control domains or IPs being seized or sinkholed. This means that an infected system may attempt to access many domains in an attempt to contact the command and control server. Therefore, the automatic detection of DGA domains is an important task, both for the sake of blocking malicious domains and identifying compromised hosts. However, many DGAs use English wordlists to generate plausibly clean-looking domain names; this makes automatic detection difficult. In this work, we devise a notion of difficulty for DGA families called the smashword score; this measures how much a DGA family looks like English words. We find that this measure accurately reflects how much a DGA family's domains look like they are made from natural English words. We then describe our new modeling approach, which is a combination of a novel recurrent neural network architecture with domain registration side information. Our experiments show the model is capable of effectively identifying domains generated by difficult DGA families. Our experiments also show that our model outperforms existing approaches, and is able to reliably detect difficult DGA families such as matsnu, suppobox, rovnix, and others. The model's performance compared to the state of the art is best for DGA families that resemble English words. We believe that this model could either be used in a standalone DGA domain detector---such as an endpoint security application---or alternately the model could be used as a part of a larger malware detection system.

Connected through hyperedge HE-1 (induced by source paper "inline detection of dga domains using side information"):
  with members:
    - "maskdga a black box evasion technique against dga classifiers and adversarial defenses"
    - "using lexical features for malicious url detection a machine learning approach"
    - "charbot a simple and effective method for evading dga classifiers"
    - "inline detection of domain generation algorithms with context sensitive word embeddings"
    - "expose a character level convolutional neural network with embeddings for detecting malicious urls file paths and registry keys"
    - "predicting domain generation algorithms with long short term memory networks"
    - "deepdga adversarially tuned domain generation and detection"

Connected through hyperedge HE-2 (induced by source paper "encrypted and covert dns queries for botnets challenges and countermeasures"):
  with members:
    - "hydras and ipfs a decentralised playground for malware"
    - "hedge efficient traffic classification of encrypted and compressed packets"
    - "a survey on malicious domains detection through dns data analysis"
    - "deepdga adversarially tuned domain generation and detection"

Connected through hyperedge HE-3 (induced by source paper "maskdga a black box evasion technique against dga classifiers and adversarial defenses"):
  with members:
    - "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"
    - "detection of malicious and low throughput data exfiltration over the dns protocol"
    - "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-4 (induced by source paper "exploiting statistical and structural features for the detection of domain generation algorithms"):
  with members:
    - "encrypted and covert dns queries for botnets challenges and countermeasures"
    - "hydras and ipfs a decentralised playground for malware"
    - "inline detection of domain generation algorithms with context sensitive word embeddings"
    - "deep contextualized word representations"
    - "a survey on malicious domains detection through dns data analysis"
    - "automatic detection of malware generated domains with recurrent neural models"
    - "don t skype type acoustic eavesdropping in voice over ip"
    - "deepdga adversarially tuned domain generation and detection"

Connected through hyperedge HE-5 (induced by source paper "hydras and ipfs a decentralised playground for malware"):
  with members:
    - "a survey on malicious domains detection through dns data analysis"
    - "developing a k ary malware using blockchain"
    - "deepdga adversarially tuned domain generation and detection"
    - "ipfs content addressed versioned p2p file system"

Connected through hyperedge HE-6 (induced by source paper "inline detection of domain generation algorithms with context sensitive word embeddings"):
  with members:
    - "deep contextualized word representations"
    - "a survey on malicious domains detection through dns data analysis"
    - "universal language model fine tuning for text classification"
    - "learned in translation contextualized word vectors"
    - "automatic detection of malware generated domains with recurrent neural models"
    - "predicting domain generation algorithms with long short term memory networks"
    - "one billion word benchmark for measuring progress in statistical language modeling"

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)