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: "robust algorithms with polynomial loss for near unanimity csps"
  Abstract: An instance of the Constraint Satisfaction Problem (CSP) is given by a family of constraints on overlapping sets of variables, and the goal is to assign values from a fixed domain to the variables so that all constraints are satisfied. In the optimization version, the goal is to maximize the number of satisfied constraints. An approximation algorithm for CSP is called robust if it outputs an assignment satisfying a $(1-g(\varepsilon))$-fraction of constraints on any $(1-\varepsilon)$-satisfiable instance, where the loss function $g$ is such that $g(\varepsilon)\rightarrow 0$ as $\varepsilon\rightarrow 0$. #R##N#We study how the robust approximability of CSPs depends on the set of constraint relations allowed in instances, the so-called constraint language. All constraint languages admitting a robust polynomial-time algorithm (with some $g$) have been characterised by Barto and Kozik, with the general bound on the loss $g$ being doubly exponential, specifically $g(\varepsilon)=O((\log\log(1/\varepsilon))/\log(1/\varepsilon))$. It is natural to ask when a better loss can be achieved: in particular, polynomial loss $g(\varepsilon)=O(\varepsilon^{1/k})$ for some constant $k$. In this paper, we consider CSPs with a constraint language having a near-unanimity polymorphism. We give two randomized robust algorithms with polynomial loss for such CSPs: one works for any near-unanimity polymorphism and the parameter $k$ in the loss depends on the size of the domain and the arity of the relations in $\Gamma$, while the other works for a special ternary near-unanimity operation called dual discriminator with $k=2$ for any domain size. In the latter case, the CSP is a common generalisation of Unique Games with a fixed domain and 2-SAT. In the former case, we use the algebraic approach to the CSP. Both cases use the standard semidefinite programming relaxation for CSP.

Connected through hyperedge HE-1 (induced by source paper "algebraic approach to promise constraint satisfaction"):
  with members:
    - "topology is relevant in a dichotomy conjecture for infinite domain constraint satisfaction problems"
    - "promises make finite constraint satisfaction problems infinitary"
    - "dichotomy for symmetric boolean pcsps"
    - "an algorithmic blend of lps and ring equations for promise csps"
    - "improved inapproximability of rainbow coloring"
    - "super polylogarithmic hypergraph coloring hardness via low degree long codes"
    - "promise constraint satisfaction algebraic structure and a symmetric boolean dichotomy"
    - "a dichotomy theorem for nonuniform csps"

Connected through hyperedge HE-2 (induced by source paper "towards a characterization of constant factor approximable finite valued csps"):
  with members:
    - "a dichotomy theorem for nonuniform csps"
    - "the proof of csp dichotomy conjecture"
    - "the complexity of finite valued csps"
    - "asking the metaquestions in constraint tractability"
    - "combinatorial optimization algorithms via polymorphisms"
    - "the complexity of general valued csps"
    - "local distribution and the symmetry gap approximability of multiway partitioning problems"
    - "robustly solvable 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.DS(Data Structures and Algorithms)