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: "robust neural malware detection models for emulation sequence learning"
  Abstract: Malicious software, or malware, presents a continuously evolving challenge in computer security. These embedded snippets of code in the form of malicious files or hidden within legitimate files cause a major risk to systems with their ability to run malicious command sequences. Malware authors even use polymorphism to reorder these commands and create several malicious variations. However, if executed in a secure environment, one can perform early malware detection on emulated command sequences. #R##N#The models presented in this paper leverage this sequential data derived via emulation in order to perform Neural Malware Detection. These models target the core of the malicious operation by learning the presence and pattern of co-occurrence of malicious event actions from within these sequences. Our models can capture entire event sequences and be trained directly using the known target labels. These end-to-end learning models are powered by two commonly used structures - Long Short-Term Memory (LSTM) Networks and Convolutional Neural Networks (CNNs). Previously proposed sequential malware classification models process no more than 200 events. Attackers can evade detection by delaying any malicious activity beyond the beginning of the file. We present specialized models that can handle extremely long sequences while successfully performing malware detection in an efficient way. We present an implementation of the Convoluted Partitioning of Long Sequences approach in order to tackle this vulnerability and operate on long sequences. We present our results on a large dataset consisting of 634,249 file sequences, with extremely long file sequences.

Connected through hyperedge HE-1 (induced by source paper "defense methods against adversarial examples for recurrent neural networks"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "textbugger generating adversarial text against real world applications"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "generating natural language adversarial examples"
    - "code vectors understanding programs through embedded abstracted symbolic traces"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "stochastic substitute training a gray box approach to craft adversarial examples against gradient obfuscation defenses"

Connected through hyperedge HE-2 (induced by source paper "integration of static and dynamic analysis for malware family classification with composite neural network"):
  with members:
    - "theoretical investigation of composite neural network"
    - "behavioral malware classification using convolutional recurrent neural networks"
    - "a family of droids android malware detection via behavioral modeling static vs dynamic analysis"
    - "attack and defense of dynamic analysis based adversarial neural malware classification models"
    - "virtual machine introspection based malware behavior profiling and family grouping"
    - "attention is all you need"
    - "dynamic analysis of executables to detect and characterize malware"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-3 (induced by source paper "scriptnet neural static analysis for malicious javascript detection"):
  with members:
    - "detecting malicious powershell commands using deep neural networks"
    - "convolutional sequence to sequence learning"
    - "a convolutional encoder model for neural machine translation"
    - "adam a method for stochastic optimization"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-4 (induced by source paper "query efficient black box attack against sequence based malware classifiers"):
  with members:
    - "learning to evade static pe machine learning malware models via reinforcement learning"
    - "generating natural language adversarial examples"
    - "black box adversarial attacks with limited queries and information"
    - "query limited black box attacks to classifiers"
    - "black box attacks against rnn based malware detection algorithms"
    - "maximum likelihood augmented discrete generative adversarial networks"
    - "generic black box end to end attack against state of the art api call based malware classifiers"
    - "evading classifiers by morphing in the dark"

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.AI(Artificial Intelligence)