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: "approximating csps with global cardinality constraints using sdp hierarchies"
  Abstract: This work is concerned with approximating constraint satisfaction problems (CSPs) with an additional global cardinality constraints. For example, \maxcut is a boolean CSP where the input is a graph $G = (V,E)$ and the goal is to find a cut $S \cup \bar S = V$ that maximizes the numberof crossing edges, $|E(S,\bar S)|$. The \maxbisection problem is a variant of \maxcut with an additional global constraint that each side of the cut has exactly half the vertices, i.e., $|S| = |V|/2$. Several other natural optimization problems like \minbisection and approximating Graph Expansion can be formulated as CSPs with global constraints. #R##N#In this work, we formulate a general approach towards approximating CSPs with global constraints using SDP hierarchies. To demonstrate the approach we present the following results: #R##N#Using the Lasserre hierarchy, we present an algorithm that runs in time $O(n^{poly(1/\epsilon)})$ that given an instance of \maxbisection with value $1-\epsilon$, finds a bisection with value $1-O(\sqrt{\epsilon})$. This approximation is near-optimal (up to constant factors in $O()$) under the Unique Games Conjecture. #R##N#By a computer-assisted proof, we show that the same algorithm also achieves a 0.85-approximation for \maxbisection, improving on the previous bound of 0.70 (note that it is \uniquegames hard to approximate better than a 0.878 factor). The same algorithm also yields a 0.92-approximation for \maxtwosat with cardinality constraints. #R##N#For every CSP with a global cardinality constraints, we present a generic conversion from integrality gap instances for the Lasserre hierarchy to a {\it dictatorship test} whose soundness is at most integrality gap. Dictatorship testing gadgets are central to hardness results for CSPs, and a generic conversion of the above nature lies at the core of the tight Unique Games based hardness result for CSPs. \cite{Raghavendra08}

Connected through hyperedge HE-1 (induced by source paper "new dependencies of hierarchies in polynomial optimization"):
  with members:
    - "optimization over the boolean hypercube via sums of nonnegative circuit polynomials"
    - "on the bit complexity of sum of squares proofs"
    - "exact tensor completion with sum of squares"
    - "fast and robust tensor decomposition with applications to dictionary learning"
    - "the power of sherali adams relaxations for general valued csps"
    - "a 1 epsilon approximation for makespan scheduling with precedence constraints using lp hierarchies"
    - "noisy tensor completion via the sum of squares hierarchy"
    - "fast spectral algorithms from sum of squares proofs tensor decomposition and planted sparse vectors"

Connected through hyperedge HE-2 (induced by source paper "list decodable learning via sum of squares"):
  with members:
    - "list decodable linear regression"
    - "efficient algorithms and lower bounds for robust linear regression"
    - "efficient algorithms for outlier robust regression"
    - "outlier robust moment estimation via sum of squares"
    - "better agnostic clustering via relaxed tensor norms"
    - "list decodable robust mean estimation and learning mixtures of spherical gaussians"
    - "mixture models robustness and sum of squares proofs"
    - "learning from untrusted data"

Connected through hyperedge HE-3 (induced by source paper "global cardinality constraints make approximating some max 2 csps harder"):
  with members:
    - "resolution lower bounds for refutation statements"
    - "a note on max k vertex cover faster fpt as smaller approximate kernel and improved approximation"
    - "a dichotomy theorem for nonuniform csps"
    - "the proof of csp dichotomy conjecture"
    - "analysis of boolean functions"
    - "better balance by being biased a 0 8776 approximation for max bisection"
    - "the complexity of global cardinality constraints"
    - "conditional hardness for approximate coloring"

Connected through hyperedge HE-4 (induced by source paper "subexponential lps approximate max cut"):
  with members:
    - "sherali adams strikes back"
    - "approximating rectangles by juntas and weakly exponential lower bounds for lp relaxations of csps"
    - "approximation limits of linear programs beyond hierarchies"
    - "rounding semidefinite programming hierarchies via global correlation"

Connected through hyperedge HE-5 (induced by source paper "information theoretic thresholds from the cavity method"):
  with members:
    - "planting colourings silently"
    - "mutual information in rank one matrix estimation"
    - "the condensation phase transition in random graph coloring"
    - "asymptotic mutual information for the two groups stochastic block model"
    - "semidefinite programs on sparse random graphs and their application to community detection"
    - "conditioning and covariance on caterpillars"
    - "chasing the k colorability threshold"
    - "on the complexity of random satisfiability problems with planted solutions"

Connected through hyperedge HE-6 (induced by source paper "optimization over the boolean hypercube via sums of nonnegative circuit polynomials"):
  with members:
    - "on the bit complexity of sum of squares proofs"
    - "a nearly tight sum of squares lower bound for the planted clique problem"
    - "on the hardest problem formulations for the 0 1 lasserre hierarchy"
    - "sum of squares lower bounds for planted clique"
    - "lower bounds on the size of semidefinite programming relaxations"
    - "sum of squares hierarchy lower bounds for symmetric formulations"
    - "sum of squares proofs and the quest toward optimal algorithms"

Connected through hyperedge HE-7 (induced by source paper "mean field approximation convex hierarchies and the optimality of correlation rounding a unified perspective"):
  with members:
    - "the vertex sample complexity of free energy is polynomial"
    - "the mean field approximation information inequalities algorithms and complexity"
    - "how to calculate partition functions using convex programming hierarchies provable bounds for variational methods"
    - "a birthday repetition theorem and complexity of approximating dense csps"
    - "sub exponential approximation schemes for csps from dense to almost sparse"
    - "conditioning and covariance on caterpillars"
    - "rounding semidefinite programming hierarchies via global correlation"

Connected through hyperedge HE-8 (induced by source paper "sticky brownian rounding and its applications to constraint satisfaction problems"):
  with members:
    - "krivine diffusions attain the goemans williamson approximation ratio"
    - "the gram schmidt walk a cure for the banaszczyk blues"
    - "constructive discrepancy minimization by walking on the edges"
    - "better balance by being biased a 0 8776 approximation for max bisection"
    - "rounding semidefinite programming hierarchies via global correlation"
    - "constructive algorithms for discrepancy minimization"
    - "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.DS(Data Structures and Algorithms)