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: "automated vulnerability detection in source code using deep representation learning"
  Abstract: Increasing numbers of software vulnerabilities are discovered every year whether they are reported publicly or discovered internally in proprietary code. These vulnerabilities can pose serious risk of exploit and result in system compromise, information leaks, or denial of service. We leveraged the wealth of C and C++ open-source code available to develop a large-scale function-level vulnerability detection system using machine learning. To supplement existing labeled vulnerability datasets, we compiled a vast dataset of millions of open-source functions and labeled it with carefully-selected findings from three different static analyzers that indicate potential exploits. The labeled dataset is available at: this https URL. Using these datasets, we developed a fast and scalable vulnerability detection tool based on deep feature representation learning that directly interprets lexed source code. We evaluated our tool on code from both real software packages and the NIST SATE IV benchmark dataset. Our results demonstrate that deep feature representation learning on source code is a promising approach for automated software vulnerability detection.

Connected through hyperedge HE-1 (induced by source paper "lucid a practical lightweight deep learning solution for ddos attack detection"):
  with members:
    - "a practical approach to detection of distributed denial of service attacks using a hybrid detection method"
    - "on evaluating adversarial robustness"
    - "understanding convolutional neural networks for text classification"
    - "deep convolutional neural networks for interpretable analysis of eeg sleep stage scoring"
    - "enorm a framework for edge node resource management"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-2 (induced by source paper "vuldeelocator a deep learning based fine grained vulnerability detector"):
  with members:
    - "leopard identifying vulnerable code for vulnerability assessment through program metrics"
    - "sysevr a framework for using deep learning to detect software vulnerabilities"
    - "vuldeepecker a deep learning based system for vulnerability detection"
    - "automated software vulnerability detection with machine learning"
    - "tensorflow a system for large scale machine learning"
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"

Connected through hyperedge HE-3 (induced by source paper "devign effective vulnerability identification by learning comprehensive program semantics via graph neural networks"):
  with members:
    - "leopard identifying vulnerable code for vulnerability assessment through program metrics"
    - "vuldeepecker a deep learning based system for vulnerability detection"
    - "automatic feature learning for vulnerability prediction"
    - "neural network based graph embedding for cross platform binary code similarity detection"
    - "learning to represent programs with graphs"
    - "gated graph sequence neural networks"
    - "discriminative embeddings of latent variable models for structured data"

Connected through hyperedge HE-4 (induced by source paper "logical segmentation of source code"):
  with members:
    - "a language agnostic model for semantic source code labeling"
    - "attention based neural text segmentation"
    - "learning to repair software vulnerabilities with generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "statically detecting vulnerabilities by processing programming languages as natural languages"):
  with members:
    - "vuldeepecker a deep learning based system for vulnerability detection"

Connected through hyperedge HE-6 (induced by source paper "towards security defect prediction with ai"):
  with members:
    - "neural arithmetic logic units"
    - "end to end prediction of buffer overruns from raw source code via neural memory networks"
    - "a survey of machine learning for big code and naturalness"
    - "a survey of symbolic execution techniques"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"

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.LG(Machine Learning)