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: "lasserre hierarchy higher eigenvalues and approximation schemes for quadratic integer programming with psd objectives"
  Abstract: We present an approximation scheme for optimizing certain Quadratic Integer Programming problems with positive semidefinite objective functions and global linear constraints. This framework includes well known graph problems such as Minimum graph bisection, Edge expansion, Uniform sparsest cut, and Small Set expansion, as well as the Unique Games problem. These problems are notorious for the existence of huge gaps between the known algorithmic results and NP-hardness results. Our algorithm is based on rounding semidefinite programs from the Lasserre hierarchy, and the analysis uses bounds for low-rank approximations of a matrix in Frobenius norm using columns of the matrix. #R##N#For all the above graph problems, we give an algorithm running in time $n^{O(r/\epsilon^2)}$ with approximation ratio $\frac{1+\epsilon}{\min\{1,\lambda_r\}}$, where $\lambda_r$ is the $r$'th smallest eigenvalue of the normalized graph Laplacian $\mathcal{L}$. In the case of graph bisection and small set expansion, the number of vertices in the cut is within lower-order terms of the stipulated bound. Our results imply $(1+O(\epsilon))$ factor approximation in time $n^{O(r^\ast/\epsilon^2)}$ where $r^\ast$ is the number of eigenvalues of $\mathcal{L}$ smaller than $1-\epsilon$. #R##N#For Unique Games, we give a factor $(1+\frac{2+\epsilon}{\lambda_r})$ approximation for minimizing the number of unsatisfied constraints in $n^{O(r/\epsilon)}$ time. This improves an earlier bound for solving Unique Games on expanders, and also shows that Lasserre SDPs are powerful enough to solve well-known integrality gap instances for the basic SDP. #R##N#We also give an algorithm for independent sets in graphs that performs well when the Laplacian does not have too many eigenvalues bigger than $1+o(1)$.

Connected through hyperedge HE-1 (induced by source paper "approximation limits of linear programs beyond hierarchies"):
  with members:
    - "approximate constraint satisfaction requires large lp relaxations"
    - "the matching polytope has exponential extension complexity"
    - "hypercontractivity sum of squares proofs and their applications"
    - "rounding semidefinite programming hierarchies via global correlation"
    - "computing a nonnegative matrix factorization provably"
    - "extended formulations non negative factorizations and randomized communication protocols"
    - "integrality gaps of linear and semi definite programming relaxations for knapsack"

Connected through hyperedge HE-2 (induced by source paper "new np hardness results for 3 coloring and 2 to 1 label cover"):
  with members:
    - "a new point of np hardness for 2 to 1 label cover"
    - "rounding semidefinite programming hierarchies via global correlation"
    - "improved inapproximability results for maximum k colorable subgraph"
    - "approximation resistant predicates from pairwise independence"
    - "conditional hardness for approximate coloring"

Connected through hyperedge HE-3 (induced by source paper "approximation algorithms for semi random graph partitioning problems"):
  with members:
    - "how to play unique games against a semi random adversary"
    - "reductions between expansion problems"
    - "are stable instances easy"
    - "how to play unique games on expanders"

Connected through hyperedge HE-4 (induced by source paper "a new point of np hardness for 2 to 1 label cover"):
  with members:
    - "rounding semidefinite programming hierarchies via global correlation"
    - "improved inapproximability results for maximum k colorable subgraph"
    - "approximation resistant predicates from pairwise independence"
    - "conditional hardness for approximate coloring"

Connected through hyperedge HE-5 (induced by source paper "topics in matrix sampling algorithms"):
  with members:
    - "a matrix hyperbolic cosine algorithm and applications"
    - "optimal column based low rank matrix reconstruction"
    - "near optimal column based matrix reconstruction"
    - "a nearly mlogn time solver for sdd linear systems"
    - "a unified framework for approximating and clustering data"
    - "algorithmic and statistical perspectives on large scale data analysis"
    - "random projections for k means clustering"
    - "row sampling for matrix algorithms via a non commutative bernstein bound"

Connected through hyperedge HE-6 (induced by source paper "rounding semidefinite programming hierarchies via global correlation"):
  with members:
    - "spectral algorithms for unique games"
    - "approximate graph coloring by semidefinite programming"

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)