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: "balancing scalability and uniformity in sat witness generator"
  Abstract: Constrained-random simulation is the predominant approach used in the industry for functional verification of complex digital designs. The effectiveness of this approach depends on two key factors: the quality of constraints used to generate test vectors, and the randomness of solutions generated from a given set of constraints. In this paper, we focus on the second problem, and present an algorithm that significantly improves the state-of-the-art of (almost-)uniform generation of solutions of large Boolean constraints. Our algorithm provides strong theoretical guarantees on the uniformity of generated solutions and scales to problems involving hundreds of thousands of variables.

Connected through hyperedge HE-1 (induced by source paper "quantitative verification of neural networks and its security applications"):
  with members:
    - "phase transition behavior of cardinality and xor constraints"
    - "adversarial examples are a natural consequence of test error in noise"
    - "strip a defence against trojan attacks on deep neural networks"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"
    - "formal security analysis of neural networks using symbolic intervals"
    - "a dual approach to scalable verification of deep networks"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "adversarial spheres"

Connected through hyperedge HE-2 (induced by source paper "faster creation of smaller test suites with snap"):
  with members:
    - "data driven search based software engineering"
    - "hyperparameter optimization for effort estimation"
    - "riot a stochastic based method for workflow scheduling in the cloud"
    - "revisiting unsupervised learning for defect prediction"
    - "beyond evolutionary algorithms for search based software engineering"
    - "a survey of symbolic execution techniques"
    - "sampling as a baseline optimizer for search based software engineering"
    - "enhancing reuse of constraint solutions to improve symbolic execution"

Connected through hyperedge HE-3 (induced by source paper "empirical bounds on linear regions of deep rectifier networks"):
  with members:
    - "towards robust locally linear deep networks"
    - "equivalent and approximate transformations of deep neural networks"
    - "on decision regions of narrow deep neural networks"
    - "tropical geometry of deep neural networks"
    - "neural networks should be wide enough to learn disconnected decision regions"
    - "resnet with one neuron hidden layers is a universal approximator"
    - "provable robustness of relu networks via maximization of linear regions"
    - "deep neural networks as 0 1 mixed integer linear programs a feasibility study"

Connected through hyperedge HE-4 (induced by source paper "constrained counting and sampling bridging the gap between theory and practice"):
  with members:
    - "approximate probabilistic inference via word level counting"
    - "distribution aware sampling and weighted model counting for sat"
    - "structured learning modulo theories"
    - "a scalable approximate model counter"
    - "taming the curse of dimensionality discrete integration by hashing and optimization"
    - "a scalable and nearly uniform generator of sat witnesses"
    - "optimization with parity constraints from binary codes to discrete integration"
    - "automated feedback generation for introductory programming assignments"

Connected through hyperedge HE-5 (induced by source paper "combining the k cnf and xor phase transitions"):
  with members:
    - "constrained sampling and counting universal hashing meets sat solving"
    - "aspects of statistical physics in computational complexity"
    - "distribution aware sampling and weighted model counting for sat"
    - "a scalable approximate model counter"
    - "a scalable and nearly uniform generator of sat witnesses"
    - "on the concentration of the number of solutions of random satisfiability formulas"

Connected through hyperedge HE-6 (induced by source paper "probabilistic model counting with short xors"):
  with members:
    - "distribution aware sampling and weighted model counting for sat"
    - "a scalable approximate model counter"
    - "taming the curse of dimensionality discrete integration by hashing and optimization"
    - "a scalable and nearly uniform generator of sat witnesses"

Connected through hyperedge HE-7 (induced by source paper "beginner s luck a language for property based generators"):
  with members:
    - "testing noninterference quickly"
    - "type targeted testing"
    - "church a language for generative models"
    - "integrating testing and interactive theorem proving"

Connected through hyperedge HE-8 (induced by source paper "sound probabilistic sat with projection"):
  with members:
    - "sampling techniques for boolean satisfiability"
    - "a scalable approximate model counter"
    - "uniform solution sampling using a constraint solver as an oracle"

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.LO(Logic in Computer Science)