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: "automatic feature learning for vulnerability prediction"
  Abstract: Code flaws or vulnerabilities are prevalent in software systems and can potentially cause a variety of problems including deadlock, information loss, or system failure. A variety of approaches have been developed to try and detect the most likely locations of such code vulnerabilities in large code bases. Most of them rely on manually designing features (e.g. complexity metrics or frequencies of code tokens) that represent the characteristics of the code. However, all suffer from challenges in sufficiently capturing both semantic and syntactic representation of source code, an important capability for building accurate prediction models. In this paper, we describe a new approach, built upon the powerful deep learning Long Short Term Memory model, to automatically learn both semantic and syntactic features in code. Our evaluation on 18 Android applications demonstrates that the prediction power obtained from our learned features is equal or even superior to what is achieved by state of the art vulnerability prediction models: 3%--58% improvement for within-project prediction and 85% for cross-project prediction.

Connected through hyperedge HE-1 (induced by source paper "v fuzz vulnerability oriented evolutionary fuzzing"):
  with members:
    - "deep reinforcement fuzzing"
    - "vuldeepecker a deep learning based system for vulnerability detection"
    - "angora efficient fuzzing by principled search"
    - "slowfuzz automated domain independent detection of algorithmic complexity vulnerabilities"
    - "faster fuzzing reinitialization with deep neural models"
    - "neural network based graph embedding for cross platform binary code similarity detection"
    - "not all bytes are equal neural byte sieve for fuzzing"
    - "a comprehensive survey of graph embedding problems techniques and applications"

Connected through hyperedge HE-2 (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"
    - "automated vulnerability detection in source code using deep representation learning"
    - "vuldeepecker a deep learning based system for vulnerability detection"
    - "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-3 (induced by source paper "automatic repair and type binding of undeclared variables using neural networks"):
  with members:
    - "learning to repair software vulnerabilities with generative adversarial networks"
    - "adversarial generation of natural language"
    - "long text generation via adversarial training with leaked information"
    - "automated correction for syntax errors in programming assignments using recurrent neural networks"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-4 (induced by source paper "relational dynamic memory networks"):
  with members:
    - "hierarchical graph representation learning with differentiable pooling"
    - "attentional multilabel learning over graphs a message passing approach"
    - "relational recurrent neural networks"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "learning deep generative models of graphs"
    - "compositional attention networks for machine reasoning"
    - "variational memory encoder decoder"
    - "a deep tree based model for software defect prediction"

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.SE(Software Engineering)