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: "vuldeepecker a deep learning based system for vulnerability detection"
  Abstract: The automatic detection of software vulnerabilities is an important research problem. However, existing solutions to this problem rely on human experts to define features and often miss many vulnerabilities (i.e., incurring high false negative rate). In this paper, we initiate the study of using deep learning-based vulnerability detection to relieve human experts from the tedious and subjective task of manually defining features. Since deep learning is motivated to deal with problems that are very different from the problem of vulnerability detection, we need some guiding principles for applying deep learning to vulnerability detection. In particular, we need to find representations of software programs that are suitable for deep learning. For this purpose, we propose using code gadgets to represent programs and then transform them into vectors, where a code gadget is a number of (not necessarily consecutive) lines of code that are semantically related to each other. This leads to the design and implementation of a deep learning-based vulnerability detection system, called Vulnerability Deep Pecker (VulDeePecker). In order to evaluate VulDeePecker, we present the first vulnerability dataset for deep learning approaches. Experimental results show that VulDeePecker can achieve much fewer false negatives (with reasonable false positives) than other approaches. We further apply VulDeePecker to 3 software products (namely Xen, Seamonkey, and Libav) and detect 4 vulnerabilities, which are not reported in the National Vulnerability Database but were "silently" patched by the vendors when releasing later versions of these products; in contrast, these vulnerabilities are almost entirely missed by the other vulnerability detection systems we experimented with.

Connected through hyperedge HE-1 (induced by source paper "meuzz smart seed scheduling for hybrid fuzzing"):
  with members:
    - "a review of machine learning applications in fuzzing"
    - "ptrix efficient hardware assisted fuzzing for cots binary"
    - "savior towards bug driven hybrid testing"
    - "learning a sat solver from single bit supervision"
    - "fairfuzz a targeted mutation strategy for increasing greybox fuzz testing coverage"
    - "evaluating fuzz testing"
    - "neuzz efficient fuzzing with neural program smoothing"
    - "angora efficient fuzzing by principled search"

Connected through hyperedge HE-2 (induced by source paper "what you see is what it means semantic representation learning of code based on visualization and transfer learning"):
  with members:
    - "code2vec learning distributed representations of code"
    - "oreo detection of clones in the twilight zone"
    - "neural code comprehension a learnable representation of code semantics"
    - "the adverse effects of code duplication in machine learning models of code"
    - "code2seq generating sequences from structured representations of code"
    - "look listen and learn"
    - "deep residual learning for image recognition"
    - "convolutional neural networks over tree structures for programming language processing"

Connected through hyperedge HE-3 (induced by source paper "learning to catch security patches"):
  with members:
    - "a manually curated dataset of fixes to vulnerabilities of open source software"
    - "patchnet a tool for deep patch classification"
    - "the coming era of alphahacking a survey of automatic software vulnerability detection exploitation and patching techniques"
    - "a practical approach to the automatic classification of security relevant commits"
    - "a survey of machine learning for big code and naturalness"
    - "survey of automated vulnerability detection and exploit generation techniques in cyber reasoning systems"
    - "impact of tool support in patch construction"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-4 (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"
    - "automated vulnerability detection in source code using deep representation learning"
    - "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-5 (induced by source paper "towards learning representations of binary executable files for security tasks"):
  with members:
    - "graph matching networks for learning the similarity of graph structured objects"
    - "neural code comprehension a learnable representation of code semantics"
    - "neural machine translation inspired binary code similarity comparison beyond function pairs"
    - "neural network based graph embedding for cross platform binary code similarity detection"
    - "robust and effective malware detection through quantitative data flow graph metrics"
    - "on the naturalness of buggy code"

Connected through hyperedge HE-6 (induced by source paper "neutaint efficient dynamic taint analysis with neural networks"):
  with members:
    - "neuro symbolic execution the feasibility of an inductive approach to symbolic execution"
    - "smartseed smart seed generation for efficient fuzzing"
    - "neuzz efficient fuzzing with neural program smoothing"
    - "angora efficient fuzzing by principled search"
    - "robustfill neural program learning under noisy i o"
    - "end to end prediction of buffer overruns from raw source code via neural memory networks"
    - "axiomatic attribution for deep networks"
    - "faster fuzzing reinitialization with deep neural models"

Connected through hyperedge HE-7 (induced by source paper "evaluating explanation methods for deep learning in security"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "interpretable deep learning under fire"
    - "sok security and privacy in machine learning"
    - "axiomatic attribution for deep networks"
    - "a unified approach to interpreting model predictions"
    - "learning important features through propagating activation differences"
    - "towards better understanding of gradient based attribution methods for deep neural networks"
    - "neural network based graph embedding for cross platform binary code similarity detection"

Connected through hyperedge HE-8 (induced by source paper "neural bug finding a study of opportunities and challenges"):
  with members:
    - "modeling vocabulary for big code machine learning"
    - "neural program repair by jointly learning to localize and repair"
    - "code2vec learning distributed representations of code"
    - "a general path based representation for predicting program properties"
    - "learning to represent edits"
    - "generative code modeling with graphs"
    - "path based function embedding and its application to specification mining"
    - "automated software vulnerability detection with machine learning"

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)