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 "a new look at survey propagation and its generalizations"):
  Abstract: This paper provides a new conceptual perspective on survey propagation, which is an iterative algorithm recently introduced by the statistical physics community that is very effective in solving random k-SAT problems even with densities close to the satisfiability threshold. We first describe how any SAT formula can be associated with a novel family of Markov random fields (MRFs), parameterized by a real number \rho \in [0,1]. We then show that applying belief propagation--a well-known ``message-passing'' technique for estimating marginal probabilities--to this family of MRFs recovers a known family of algorithms, ranging from pure survey propagation at one extreme (\rho = 1) to standard belief propagation on the uniform distribution over SAT assignments at the other extreme (\rho = 0). Configurations in these MRFs have a natural interpretation as partial satisfiability assignments, on which a partial order can be defined. We isolate cores as minimal elements in this partial ordering, which are also fixed points of survey propagation and the only assignments with positive probability in the MRF for \rho=1. Our experimental results for k=3 suggest that solutions of random formulas typically do not possess non-trivial cores. This makes it necessary to study the structure of the space of partial assignments for \rho 0 can be viewed as a ``smoothed'' version of the uniform distribution over satisfying assignments (\rho=0). Finally, we isolate properties of Gibbs sampling and message-passing algorithms that are typical for an ensemble of k-SAT problems.

Includes members:
  Member "belief optimization for binary networks a stable alternative to loopy belief propagation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "lifted hybrid variational inference")
  Member "on the solution space geometry of random constraint satisfaction problems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "quantitative verification of neural networks and its security applications")
  Member "low density constructions can achieve the wyner ziv and gelfand pinsker bounds":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "analytical framework of ldgm based iterative quantization with decimation")
  Member "lossy source encoding via message passing and decimation over generalized codewords of ldgm codes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "low density majority codes and the problem of graceful degradation")
  Member "the threshold for random k sat is 2 k ln2 o k":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the power of choice for random satisfiability")
  Member "typical random 3 sat formulae and the satisfiability threshold":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "satisfiability threshold for power law random 2 sat in configuration model")
  Member "on local equilibrium equations for clustering states":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the backtracking survey propagation algorithm for solving random k sat problems")
  Member "survey propagation an algorithm for satisfiability":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "pdp a general neural framework for learning constraint satisfaction solvers")

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.CC(Computational Complexity)