human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "learning from context exploiting and interpreting file path information for better malware detection"):
  Abstract: Machine learning (ML) used for static portable executable (PE) malware detection typically employs per-file numerical feature vector representations as input with one or more target labels during training. However, there is much orthogonal information that can be gleaned from the \textit{context} in which the file was seen. In this paper, we propose utilizing a static source of contextual information -- the path of the PE file -- as an auxiliary input to the classifier. While file paths are not malicious or benign in and of themselves, they do provide valuable context for a malicious/benign determination. Unlike dynamic contextual information, file paths are available with little overhead and can seamlessly be integrated into a multi-view static ML detector, yielding higher detection rates at very high throughput with minimal infrastructural changes. Here we propose a multi-view neural network, which takes feature vectors from PE file content as well as corresponding file paths as inputs and outputs a detection score. To ensure realistic evaluation, we use a dataset of approximately 10 million samples -- files and file paths from user endpoints of an actual security vendor network. We then conduct an interpretability analysis via LIME modeling to ensure that our classifier has learned a sensible representation and see which parts of the file path most contributed to change in the classifier's score. We find that our model learns useful aspects of the file path for classification, while also learning artifacts from customers testing the vendor's product, e.g., by downloading a directory of malware samples each named as their hash. We prune these artifacts from our test dataset and demonstrate reductions in false negative rate of 32.3% at a $10^{-3}$ false positive rate (FPR) and 33.1% at $10^{-4}$ FPR, over a similar topology single input PE file content only model.

Includes members:
  Member "aloha auxiliary loss optimization for hypothesis augmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "automatic malware description via attribute tagging and similarity embedding")
  Member "ember an open dataset for training static pe malware machine learning models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "automatic generation of adversarial examples for interpreting malware classifiers")
  Member "meade towards a malicious email attachment detection engine":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "automatic malware description via attribute tagging and similarity embedding")
  Member "a deep learning approach to fast format agnostic detection of malicious web content":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "automatic malware description via attribute tagging and similarity embedding")
  Member "microsoft malware classification challenge":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "interpreting machine learning malware detectors which leverage n gram analysis")
  Member "expose a character level convolutional neural network with embeddings for detecting malicious urls file paths and registry keys":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "nested multiple instance learning in modelling of http network traffic")
  Member "a multi view context aware approach to android malware detection and malicious code localization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "droidetec android malware detection and malicious code localization through deep learning")
  Member "in2i unsupervised multi image to image translation using generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generating and aligning from data geometries with generative adversarial networks")

Question: Please tell me which class the source paper 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)