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: "novel feature extraction selection and fusion for effective malware family classification"
  Abstract: Modern malware is designed with mutation characteristics, namely polymorphism and metamorphism, which causes an enormous growth in the number of variants of malware samples. Categorization of malware samples on the basis of their behaviors is essential for the computer security community, because they receive huge number of malware everyday, and the signature extraction process is usually based on malicious parts characterizing malware families. Microsoft released a malware classification challenge in 2015 with a huge dataset of near 0.5 terabytes of data, containing more than 20K malware samples. The analysis of this dataset inspired the development of a novel paradigm that is effective in categorizing malware variants into their actual family groups. This paradigm is presented and discussed in the present paper, where emphasis has been given to the phases related to the extraction, and selection of a set of novel features for the effective representation of malware samples. Features can be grouped according to different characteristics of malware behavior, and their fusion is performed according to a per-class weighting paradigm. The proposed method achieved a very high accuracy ($\approx$ 0.998) on the Microsoft Malware Challenge dataset.

Connected through hyperedge HE-1 (induced by source paper "learning from context exploiting and interpreting file path information for better malware detection"):
  with members:
    - "aloha auxiliary loss optimization for hypothesis augmentation"
    - "ember an open dataset for training static pe malware machine learning models"
    - "meade towards a malicious email attachment detection engine"
    - "a deep learning approach to fast format agnostic detection of malicious web content"
    - "microsoft malware classification challenge"
    - "expose a character level convolutional neural network with embeddings for detecting malicious urls file paths and registry keys"
    - "a multi view context aware approach to android malware detection and malicious code localization"
    - "in2i unsupervised multi image to image translation using generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "malware classification using deep learning based feature extraction and wrapper based feature selection technique"):
  with members:
    - "detecting and classifying android malware using static analysis along with creator information"
    - "a survey of the recent architectures of deep convolutional neural networks"
    - "adversarial deep learning for robust detection of binary encoded malware"
    - "deepsign deep learning for automatic malware signature generation and classification"
    - "efficient estimation of word representations in vector space"
    - "malware classification based on call graph clustering"

Connected through hyperedge HE-3 (induced by source paper "survey of machine learning techniques for malware analysis"):
  with members:
    - "towards generic deobfuscation of windows api calls"
    - "a survey of symbolic execution techniques"
    - "back to the future malware detection with temporally consistent labels"
    - "robust and effective malware detection through quantitative data flow graph metrics"
    - "deep neural network based malware detection using two dimensional binary program features"

Connected through hyperedge HE-4 (induced by source paper "detection of advanced malware by machine learning techniques"):
  with members:
    - "grouping the executables to detect malware with high accuracy"
    - "an effective approach for classification of advanced malware with high accuracy"

Connected through hyperedge HE-5 (induced by source paper "deep learning at the shallow end malware classification for non domain experts"):
  with members:
    - "microsoft malware classification challenge"
    - "network intell enabling the non expert analysis of large volumes of intercepted network traffic"
    - "recent trends in deep learning based natural language processing"
    - "increasing digital investigator availability through efficient workflow management and automation"
    - "integration of ether unpacker into ragpicker for plugin based malware analysis and identification"
    - "battling the digital forensic backlog through data deduplication"
    - "tiered forensic methodology model for digital field triage by non digital evidence specialists"
    - "deepsign deep learning for automatic malware signature generation and classification"

Connected through hyperedge HE-6 (induced by source paper "end to end deep neural networks and transfer learning for automatic analysis of nation state malware"):
  with members:
    - "when coding style survives compilation de anonymizing programmers from executable binaries"
    - "deepapt nation state apt attribution using end to end deep neural networks"
    - "on the feasibility of malware authorship attribution"
    - "deepsign deep learning for automatic malware signature generation and classification"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "visualizing and understanding convolutional networks"
    - "representation learning a review and new perspectives"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-7 (induced by source paper "comparison of deep learning and the classical machine learning algorithm for the malware detection"):
  with members:
    - "an effective approach for classification of advanced malware with high accuracy"
    - "a multi task learning model for malware classification with useful file access pattern from api call sequence"
    - "deepsign deep learning for automatic malware signature generation and classification"
    - "deep neural network based malware detection using two dimensional binary program features"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"
    - "evolution and detection of polymorphic and metamorphic malwares a survey"

Connected through hyperedge HE-8 (induced by source paper "an investigation of a deep learning based malware detection system"):
  with members:
    - "group wise classification approach to improve android malicious apps detection accuracy"
    - "improving the detection accuracy of unknown malware by partitioning the executables in groups"
    - "a multi task learning model for malware classification with useful file access pattern from api call sequence"
    - "deepsign deep learning for automatic malware signature generation and classification"
    - "deep neural network based malware detection using two dimensional binary program features"

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)