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: "how to play unique games against a semi random adversary"
  Abstract: In this paper, we study the average case complexity of the Unique Games problem. We propose a natural semi-random model, in which a unique game instance is generated in several steps. First an adversary selects a completely satisfiable instance of Unique Games, then she chooses an epsilon-fraction of all edges, and finally replaces ("corrupts") the constraints corresponding to these edges with new constraints. If all steps are adversarial, the adversary can obtain any (1-epsilon) satisfiable instance, so then the problem is as hard as in the worst case. In our semi-random model, one of the steps is random, and all other steps are adversarial. We show that known algorithms for unique games (in particular, all algorithms that use the standard SDP relaxation) fail to solve semi-random instances of Unique Games. #R##N#We present an algorithm that with high probability finds a solution satisfying a (1-delta) fraction of all constraints in semi-random instances (we require that the average degree of the graph is Omega(log k). To this end, we consider a new non-standard SDP program for Unique Games, which is not a relaxation for the problem, and show how to analyze it. We present a new rounding scheme that simultaneously uses SDP and LP solutions, which we believe is of independent interest. #R##N#Our result holds only for epsilon less than some absolute constant. We prove that if epsilon > 1/2, then the problem is hard in one of the models, the result assumes the 2-to-2 conjecture. #R##N#Finally, we study semi-random instances of Unique Games that are at most (1-epsilon) satisfiable. We present an algorithm that with high probability, distinguishes between the case when the instance is a semi-random instance and the case when the instance is an (arbitrary) (1-delta) satisfiable instance if epsilon > c delta.

Connected through hyperedge HE-1 (induced by source paper "towards learning sparsely used dictionaries with arbitrary supports"):
  with members:
    - "outlier robust moment estimation via sum of squares"
    - "clustering semi random mixtures of gaussians"
    - "an improved analysis of the er spud dictionary learning algorithm"
    - "polynomial time tensor decompositions with sum of squares"
    - "finding a sparse vector in a subspace linear sparsity using alternating directions"
    - "on the effect of randomness on planted 3 coloring models"
    - "simple efficient and neural algorithms for sparse coding"
    - "a semidefinite program for unbalanced multisection in the stochastic block model"

Connected through hyperedge HE-2 (induced by source paper "a new algorithm for the robust semi random independent set problem"):
  with members:
    - "does robustness imply tractability a lower bound for planted clique in the semi random model"
    - "learning from untrusted data"
    - "constant factor approximation for balanced cut in the pie model"
    - "grothendieck type inequalities in combinatorial optimization"

Connected through hyperedge HE-3 (induced by source paper "constant factor approximation for balanced cut in the pie model"):
  with members:
    - "bilu linial stable instances of max cut and minimum multiway cut"
    - "on the practically interesting instances of maxcut"
    - "clustering partially observed graphs via convex optimization"
    - "center based clustering under perturbation stability"
    - "reductions between expansion problems"
    - "are stable instances easy"

Connected through hyperedge HE-4 (induced by source paper "approximation algorithms for semi random graph partitioning problems"):
  with members:
    - "lasserre hierarchy higher eigenvalues and approximation schemes for quadratic integer programming with psd objectives"
    - "reductions between expansion problems"
    - "are stable instances easy"
    - "how to play unique games on expanders"

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.DS(Data Structures and Algorithms)