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: "malware classification based on call graph clustering"
  Abstract: Each day, anti-virus companies receive tens of thousands samples of potentially harmful executables. Many of the malicious samples are variations of previously encountered malware, created by their authors to evade pattern-based detection. Dealing with these large amounts of data requires robust, automatic detection approaches. This paper studies malware classification based on call graph clustering. By representing malware samples as call graphs, it is possible to abstract certain variations away, and enable the detection of structural similarities between samples. The ability to cluster similar samples together will make more generic detection techniques possible, thereby targeting the commonalities of the samples within a cluster. To compare call graphs mutually, we compute pairwise graph similarity scores via graph matchings which approximately minimize the graph edit distance. Next, to facilitate the discovery of similar malware samples, we employ several clustering algorithms, including k-medoids and DBSCAN. Clustering experiments are conducted on a collection of real malware samples, and the results are evaluated against manual classifications provided by human malware analysts. Experiments show that it is indeed possible to accurately detect malware families via call graph clustering. We anticipate that in the future, call graphs can be used to analyse the emergence of new malware families, and ultimately to automate implementation of generic detection schemes.

Connected through hyperedge HE-1 (induced by source paper "new era of deeplearning based malware intrusion detection the malware detection and prediction based on deep learning"):
  with members:
    - "feudal networks for hierarchical reinforcement learning"
    - "improved training of wasserstein gans"
    - "long text generation via adversarial training with leaked information"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "mamadroid detecting android malware by building markov chains of behavioral models"
    - "xgboost a scalable tree boosting system"
    - "very deep convolutional networks for text classification"
    - "n opcode analysis for android malware classification and categorization"

Connected through hyperedge HE-2 (induced by source paper "generation evaluation of adversarial examples for malware obfuscation"):
  with members:
    - "explaining vulnerabilities of deep learning to adversarial malware binaries"
    - "deceiving end to end deep learning malware detectors using adversarial examples"
    - "microsoft malware classification challenge"
    - "black box attacks against rnn based malware detection algorithms"
    - "imbalanced malware images classification a cnn based approach"
    - "generating adversarial malware examples for black box attacks based on gan"
    - "adversarial perturbations against deep neural networks for malware classification"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-3 (induced by source paper "jgrapht a java library for graph data structures and algorithms"):
  with members:
    - "graphblast a high performance linear algebra based graph framework on the gpu"
    - "mathematical foundations of the graphblas"
    - "tight lower and upper bounds for the complexity of canonical colour refinement"
    - "gunrock a high performance graph processing library on the gpu"
    - "networkit a tool suite for large scale complex network analysis"
    - "pagerank beyond the web"
    - "listing all maximal cliques in sparse graphs in near optimal time"

Connected through hyperedge HE-4 (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"
    - "novel feature extraction selection and fusion for effective malware family classification"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-5 (induced by source paper "network based analysis and classification of malware using behavioral artifacts ordering"):
  with members:
    - "mamadroid detecting android malware by building markov chains of behavioral models"
    - "malicious behavior detection using windows audit logs"

Connected through hyperedge HE-6 (induced by source paper "multiple instance learning for malware classification"):
  with members:
    - "novel feature extraction selection and fusion for effective malware family classification"
    - "algorithms for learning kernels based on centered alignment"
    - "multi instance learning by treating instances as non i i d samples"
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-7 (induced by source paper "crowdsource automated inference of high level malware functionality from low level symbols using a crowd trained machine learning model"):
  with members:
    - "a generalization of the noisy or model"

Connected through hyperedge HE-8 (induced by source paper "malware task identification a data driven approach"):
  with members:
    - "crowdsource automated inference of high level malware functionality from low level symbols using a crowd trained machine learning model"

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)