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: "hiding satisfying assignments two are better than one"
  Abstract: The evaluation of incomplete satisfiability solvers depends critically on the availability of hard satisfiable instances. A plausible source of such instances consists of random k-SAT formulas whose clauses are chosen uniformly from among all clauses satisfying some randomly chosen truth assignment A. Unfortunately, instances generated in this manner tend to be relatively easy and can be solved efficiently by practical heuristics. Roughly speaking, as the formula's density increases, for a number of different algorithms, A acts as a stronger and stronger attractor. Motivated by recent results on the geometry of the space of satisfying truth assignments of random k-SAT and NAE-k-SAT formulas, we introduce a simple twist on this basic model, which appears to dramatically increase its hardness. Namely, in addition to forbidding the clauses violated by the hidden assignment A, we also forbid the clauses violated by its complement, so that both A and complement of A are satisfying. It appears that under this "symmetrization'' the effects of the two attractors largely cancel out, making it much harder for algorithms to find any truth assignment. We give theoretical and experimental evidence supporting this assertion.

Connected through hyperedge HE-1 (induced by source paper "community based 3 sat formulas with a predefined solution"):
  with members:
    - "on the hardness of sat with community structure"

Connected through hyperedge HE-2 (induced by source paper "the mutual information of ldgm codes"):
  with members:
    - "the computer science and physics of community detection landscapes phase transitions and hardness"
    - "multi layer generalized linear estimation"
    - "i mmse relations in random linear estimation and a sub extensive interpolation method"
    - "threshold saturation of spatially coupled sparse superposition codes for all memoryless channels"
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "community detection thresholds and the weak ramanujan property"
    - "sharp bounds for optimal decoding of low density parity check codes"
    - "tight bounds for ldpc and ldgm codes under map decoding"

Connected through hyperedge HE-3 (induced by source paper "subsampled power iteration a unified algorithm for block models and planted csp s"):
  with members:
    - "exact recovery in the stochastic block model"
    - "spectral redemption in clustering sparse networks"
    - "more data speeds up training time in learning halfspaces over sparse vectors"
    - "on the complexity of random satisfiability problems with planted solutions"
    - "community detection thresholds and the weak ramanujan property"
    - "graph spectra and the detectability of community structure in networks"
    - "using more data to speed up training time"

Connected through hyperedge HE-4 (induced by source paper "on the complexity of random satisfiability problems with planted solutions"):
  with members:
    - "how to refute a random csp"
    - "statistical query algorithms for stochastic convex optimization"
    - "subsampled power iteration a unified algorithm for block models and planted csp s"
    - "human computable passwords"
    - "spectral redemption in clustering sparse networks"
    - "more data speeds up training time in learning halfspaces over sparse vectors"
    - "community detection thresholds and the weak ramanujan property"
    - "a complete characterization of statistical query learning with applications to evolvability"

Connected through hyperedge HE-5 (induced by source paper "generating hard satisfiable formulas by hiding solutions deceptively"):
  with members:
    - "heuristic average case analysis of the backtrack resolution of random 3 satisfiability instances"
    - "approximate analysis of search algorithms with physical methods"
    - "the threshold for random k sat is 2 k ln2 o k"

Connected through hyperedge HE-6 (induced by source paper "a simple model to generate hard satisfiable instances"):
  with members:
    - "many hard examples in exact phase transitions with application to generating hard satisfiable instances"
    - "exact phase transitions in random constraint satisfaction problems"

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.AI(Artificial Intelligence)