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: "slowfuzz automated domain independent detection of algorithmic complexity vulnerabilities"
  Abstract: Algorithmic complexity vulnerabilities occur when the worst-case time/space complexity of an application is significantly higher than the respective average case for particular user-controlled inputs. When such conditions are met, an attacker can launch Denial-of-Service attacks against a vulnerable application by providing inputs that trigger the worst-case behavior. Such attacks have been known to have serious effects on production systems, take down entire websites, or lead to bypasses of Web Application Firewalls.   Unfortunately, existing detection mechanisms for algorithmic complexity vulnerabilities are domain-specific and often require significant manual effort. In this paper, we design, implement, and evaluate SlowFuzz, a domain-independent framework for automatically finding algorithmic complexity vulnerabilities. SlowFuzz automatically finds inputs that trigger worst-case algorithmic behavior in the tested binary. SlowFuzz uses resource-usage-guided evolutionary search techniques to automatically find inputs that maximize computational resource utilization for a given application.   We demonstrate that SlowFuzz successfully generates inputs that match the theoretical worst-case performance for several well-known algorithms. SlowFuzz was also able to generate a large number of inputs that trigger different algorithmic complexity vulnerabilities in real-world applications, including various zip parsers used in antivirus software, regular expression libraries used in Web Application Firewalls, as well as hash table implementations used in Web applications. In particular, SlowFuzz generated inputs that achieve 300-times slowdown in the decompression routine of the bzip utility, discovered regular expressions that exhibit matching times exponential in the input size, and also managed to automatically produce inputs that trigger a high number of collisions in PHP's default hashtable implementation.

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"
    - "badger complexity analysis with fuzzing and symbolic execution"
    - "discriminating traces with time"
    - "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 "gasfuzz generating high gas consumption inputs to avoid out of gas vulnerability"):
  with members:
    - "evmfuzz differential fuzz testing of ethereum virtual machine"
    - "the ico phenomenon and its relationships with ethereum smart contract environment"
    - "gastap a gas analyzer for smart contracts"
    - "contractfuzzer fuzzing smart contracts for vulnerability detection"
    - "a semantic framework for the security analysis of ethereum smart contracts"
    - "enfuzz from ensemble learning to ensemble fuzzing"
    - "vandal a scalable security analysis framework for smart contracts"
    - "securify practical security analysis of smart contracts"

Connected through hyperedge HE-3 (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"
    - "automatic feature learning for vulnerability prediction"
    - "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-4 (induced by source paper "synfuzz efficient concolic execution via branch condition synthesis"):
  with members:
    - "fairfuzz a targeted mutation strategy for increasing greybox fuzz testing coverage"
    - "evaluating fuzz testing"
    - "angora efficient fuzzing by principled search"
    - "program synthesis using conflict driven learning"
    - "not all bytes are equal neural byte sieve for fuzzing"
    - "learn fuzz machine learning for input fuzzing"
    - "deepcoder learning to write programs"
    - "synthesizing program input grammars"

Connected through hyperedge HE-5 (induced by source paper "automatic techniques to systematically discover new heap exploitation primitives"):
  with members:
    - "evaluating fuzz testing"
    - "angora efficient fuzzing by principled search"
    - "automatic heap layout manipulation for exploitation"
    - "freeguard a faster secure heap allocator"
    - "deepxplore automated whitebox testing of deep learning systems"

Connected through hyperedge HE-6 (induced by source paper "evolutionary fuzzing of android os vendor system services"):
  with members:
    - "evaluating fuzz testing"
    - "evomaster evolutionary multi context automated system test generation"

Connected through hyperedge HE-7 (induced by source paper "neuzz efficient fuzzing with neural program smoothing"):
  with members:
    - "neuro symbolic execution the feasibility of an inductive approach to symbolic execution"
    - "deep reinforcement fuzzing"
    - "angora efficient fuzzing by principled search"
    - "faster fuzzing reinitialization with deep neural models"
    - "neurogenesis deep learning extending deep networks to accommodate new classes"
    - "not all bytes are equal neural byte sieve for fuzzing"
    - "measuring catastrophic forgetting in neural networks"
    - "pathnet evolution channels gradient descent in super neural networks"

Connected through hyperedge HE-8 (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"
    - "badger complexity analysis with fuzzing and symbolic execution"
    - "angora efficient fuzzing by principled search"
    - "neural network based graph embedding for cross platform binary code similarity detection"
    - "learn fuzz machine learning for input fuzzing"

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)