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: "vxa a virtual architecture for durable compressed archives"
  Abstract: Data compression algorithms change frequently, and obsolete decoders do not always run on new hardware and operating systems, threatening the long-term usability of content archived using those algorithms. Re-encoding content into new formats is cumbersome, and highly undesirable when lossy compression is involved. Processor architectures, in contrast, have remained comparatively stable over recent decades. VXA, an archival storage system designed around this observation, archives executable decoders along with the encoded content it stores. VXA decoders run in a specialized virtual machine that implements an OS-independent execution environment based on the standard x86 architecture. The VXA virtual machine strictly limits access to host system services, making decoders safe to run even if an archive contains malicious code. VXA's adoption of a "native" processor architecture instead of type-safe language technology allows reuse of existing "hand-optimized" decoders in C and assembly language, and permits decoders access to performance-enhancing architecture features such as vector processing instructions. The performance cost of VXA's virtualization is typically less than 15% compared with the same decoders running natively. The storage cost of archived decoders, typically 30-130KB each, can be amortized across many archived files sharing the same compression method.

Connected through hyperedge HE-1 (induced by source paper "meade towards a malicious email attachment detection engine"):
  with members:
    - "ember an open dataset for training static pe malware machine learning models"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "expose a character level convolutional neural network with embeddings for detecting malicious urls file paths and registry keys"
    - "open set intrusion recognition for fine grained attack categorization"
    - "facial attributes accuracy and adversarial robustness"
    - "a survey of stealth malware attacks mitigation measures and steps toward autonomous open world solutions"
    - "xgboost a scalable tree boosting system"
    - "adversarial diversity and hard positive generation"

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.DL(Digital Libraries)