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 "bit vector model counting using statistical estimation"):
  Abstract: Approximate model counting for bit-vector SMT formulas (generalizing \#SAT) has many applications such as probabilistic inference and quantitative information-flow security, but it is computationally difficult. Adding random parity constraints (XOR streamlining) and then checking satisfiability is an effective approximation technique, but it requires a prior hypothesis about the model count to produce useful results. We propose an approach inspired by statistical estimation to continually refine a probabilistic estimate of the model count for a formula, so that each XOR-streamlined query yields as much information as possible. We implement this approach, with an approximate probability model, as a wrapper around an off-the-shelf SMT solver or SAT solver. Experimental results show that the implementation is faster than the most similar previous approaches which used simpler refinement strategies. The technique also lets us model count formulas over floating-point constraints, which we demonstrate with an application to a vulnerability in differential privacy mechanisms.

Includes members:
  Member "combining the k cnf and xor phase transitions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "quantitative verification of neural networks and its security applications")
  Member "the hard problems are almost everywhere for random cnf xor formulas":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "quantitative verification of neural networks and its security applications")
  Member "sound probabilistic sat with projection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "approximate model counting sparse xor constraints and minimum distance")
  Member "approximate probabilistic inference via word level counting":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient search based weighted model integration")
  Member "approximate counting in smt and value estimation for probabilistic programs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "semiring programming a declarative framework for generalized sum product problems")
  Member "a scalable approximate model counter":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "quantitative verification of neural networks and its security applications")
  Member "the good old davis putnam procedure helps counting models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "estimating the density of states of boolean satisfiability problems on classical and quantum computing platforms")

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)