human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "hotfuzz discovering algorithmic denial of service vulnerabilities through guided micro fuzzing"):
  Abstract: Contemporary fuzz testing techniques focus on identifying memory corruption vulnerabilities that allow adversaries to achieve either remote code execution or information disclosure. Meanwhile, Algorithmic Complexity (AC)vulnerabilities, which are a common attack vector for denial-of-service attacks, remain an understudied threat. In this paper, we present HotFuzz, a framework for automatically discovering AC vulnerabilities in Java libraries. HotFuzz uses micro-fuzzing, a genetic algorithm that evolves arbitrary Java objects in order to trigger the worst-case performance for a method under test. We define Small Recursive Instantiation (SRI) as a technique to derive seed inputs represented as Java objects to micro-fuzzing. After micro-fuzzing, HotFuzz synthesizes test cases that triggered AC vulnerabilities into Java programs and monitors their execution in order to reproduce vulnerabilities outside the fuzzing framework. HotFuzz outputs those programs that exhibit high CPU utilization as witnesses for AC vulnerabilities in a Java library. We evaluate HotFuzz over the Java Runtime Environment (JRE), the 100 most popular Java libraries on Maven, and challenges contained in the DARPA Space and Time Analysis for Cybersecurity (STAC) program. We evaluate SRI's effectiveness by comparing the performance of micro-fuzzing with SRI, measured by the number of AC vulnerabilities detected, to simply using empty values as seed inputs. In this evaluation, we verified known AC vulnerabilities, discovered previously unknown AC vulnerabilities that we responsibly reported to vendors, and received confirmation from both IBM and Oracle. Our results demonstrate that micro-fuzzing finds AC vulnerabilities in real-world software, and that micro-fuzzing with SRI-derived seed inputs outperforms using empty values.

Includes members:
  Member "evaluating fuzz testing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "meuzz smart seed scheduling for hybrid fuzzing")
  Member "improving function coverage with munch a hybrid fuzzing and directed symbolic execution approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "verifying software vulnerabilities in iot cryptographic protocols")
  Member "badger complexity analysis with fuzzing and symbolic execution":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "superion grammar aware greybox fuzzing")
  Member "discriminating traces with time":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient detection and quantification of timing leaks with neural networks")
  Member "slowfuzz automated domain independent detection of algorithmic complexity vulnerabilities":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gasfuzz generating high gas consumption inputs to avoid out of gas vulnerability")
  Member "rise of the hacrs augmenting autonomous cyber reasoning systems with human assistance":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an observational investigation of reverse engineers processes")
  Member "static analysis for regular expression denial of service attacks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "static detection of dos vulnerabilities in programs that use regular expressions extended version")

Question: Please tell me which class the source paper 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)