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: "badger complexity analysis with fuzzing and symbolic execution"
  Abstract: Hybrid testing approaches that involve fuzz testing and symbolic execution have shown promising results in achieving high code coverage, uncovering subtle errors and vulnerabilities in a variety of software applications. In this paper we describe Badger - a new hybrid approach for complexity analysis, with the goal of discovering vulnerabilities which occur when the worst-case time or space complexity of an application is significantly higher than the average case.     Badger uses fuzz testing to generate a diverse set of inputs that aim to increase not only coverage but also a resource-related cost associated with each path. Since fuzzing may fail to execute deep program paths due to its limited knowledge about the conditions that influence these paths, we complement the analysis with a symbolic execution, which is also customized to search for paths that increase the resource-related cost. Symbolic execution is particularly good at generating inputs that satisfy various program conditions but by itself suffers from path explosion. Therefore, Badger uses fuzzing and symbolic execution in tandem, to leverage their benefits and overcome their weaknesses.     We implemented our approach for the analysis of Java programs, based on Kelinci and Symbolic PathFinder. We evaluated Badger on Java applications, showing that our approach is significantly faster in generating worst-case executions compared to fuzzing or symbolic execution on their own.

Connected through hyperedge HE-1 (induced by source paper "hotfuzz discovering algorithmic denial of service vulnerabilities through guided micro fuzzing"):
  with members:
    - "evaluating fuzz testing"
    - "improving function coverage with munch a hybrid fuzzing and directed symbolic execution approach"
    - "discriminating traces with time"
    - "slowfuzz automated domain independent detection of algorithmic complexity vulnerabilities"
    - "rise of the hacrs augmenting autonomous cyber reasoning systems with human assistance"
    - "static analysis for regular expression denial of service attacks"

Connected through hyperedge HE-2 (induced by source paper "superion grammar aware greybox fuzzing"):
  with members:
    - "fairfuzz a targeted mutation strategy for increasing greybox fuzz testing coverage"
    - "evaluating fuzz testing"
    - "contractfuzzer fuzzing smart contracts for vulnerability detection"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "angora efficient fuzzing by principled search"
    - "slowfuzz automated domain independent detection of algorithmic complexity vulnerabilities"
    - "neural network based graph embedding for cross platform binary code similarity detection"
    - "learn fuzz machine learning for input fuzzing"

Connected through hyperedge HE-3 (induced by source paper "diffuzz differential fuzzing for side channel analysis"):
  with members:
    - "fairfuzz a targeted mutation strategy for increasing greybox fuzz testing coverage"
    - "stads software testing as species discovery"
    - "stacco differentially analyzing side channel traces for detecting ssl tls vulnerabilities in secure enclaves"

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)