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: "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.

Connected through hyperedge HE-1 (induced by source paper "streamlining variational inference for constraint satisfaction problems"):
  with members:
    - "adversarial constraint learning for structured prediction"
    - "the backtracking survey propagation algorithm for solving random k sat problems"
    - "label free supervision of neural networks with physics and domain knowledge"
    - "closing the gap between short and long xors for model counting"
    - "survey propagation an algorithm for satisfiability"

Connected through hyperedge HE-2 (induced by source paper "a linear algebra formulation for boolean satisfiability testing"):
  with members:
    - "the p versus np problem"
    - "a scalable algorithm for minimal unsatisfiable core extraction"

Connected through hyperedge HE-3 (induced by source paper "the backtracking survey propagation algorithm for solving random k sat problems"):
  with members:
    - "on the solution space geometry of random constraint satisfaction problems"
    - "some remarks on the survey decimation algorithm for k satisfiability"
    - "threshold values of random k sat from the cavity method"
    - "typical random 3 sat formulae and the satisfiability threshold"
    - "on local equilibrium equations for clustering states"
    - "survey propagation an algorithm for satisfiability"

Connected through hyperedge HE-4 (induced by source paper "on sparse discretization for graphical games"):
  with members:
    - "graphical models for game theory"
    - "interdependent defense games modeling interdependent security under deliberate attacks"
    - "computing approximate nash equilibria and robust best responses using sampling"
    - "discretized multinomial distributions and nash equilibria in anonymous games"
    - "settling the complexity of computing two player nash equilibria"
    - "computing nash equilibria approximation and smoothed complexity"
    - "pure nash equilibria hard and easy games"
    - "constraint optimization and statistical mechanics"

Connected through hyperedge HE-5 (induced by source paper "revisiting algebra and complexity of inference in graphical models"):
  with members:
    - "on map inference by mwss on perfect graphs"
    - "loopy belief propagation for approximate inference an empirical study"
    - "hybrid processing of beliefs and constraints"
    - "survey propagation revisited"
    - "robust max product belief propagation"
    - "approximating the permanent with belief propagation"
    - "an algebraic graphical model for decision with uncertainties feasibilities and utilities"
    - "complexity results and approximation strategies for map explanations"

Connected through hyperedge HE-6 (induced by source paper "algebra of inference in graphical models revisited"):
  with members:
    - "on map inference by mwss on perfect graphs"
    - "loopy belief propagation for approximate inference an empirical study"
    - "hybrid processing of beliefs and constraints"
    - "robust max product belief propagation"
    - "approximating the permanent with belief propagation"
    - "complexity results and approximation strategies for map explanations"

Connected through hyperedge HE-7 (induced by source paper "perturbed message passing for constraint satisfaction problems"):
  with members:
    - "revisiting algebra and complexity of inference in graphical models"
    - "survey propagation revisited"
    - "belief propagation for continuous state spaces stochastic message passing with quantitative guarantees"
    - "xml representation of constraint networks format xcsp 2 1"
    - "solving constraint satisfaction problems through belief propagation guided decimation"
    - "survey propagation an algorithm for satisfiability"

Connected through hyperedge HE-8 (induced by source paper "relaxed survey propagation for the weighted maximum satisfiability problem"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "survey propagation revisited"
    - "survey propagation an algorithm for satisfiability"

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.CC(Computational Complexity)