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: "lower bounds on the size of semidefinite programming relaxations"
  Abstract: We introduce a method for proving lower bounds on the efficacy of semidefinite programming (SDP) relaxations for combinatorial problems. In particular, we show that the cut, TSP, and stable set polytopes on $n$-vertex graphs are not the linear image of the feasible region of any SDP (i.e., any spectrahedron) of dimension less than $2^{n^c}$, for some constant $c > 0$. This result yields the first super-polynomial lower bounds on the semidefinite extension complexity of any explicit family of polytopes. #R##N#Our results follow from a general technique for proving lower bounds on the positive semidefinite rank of a matrix. To this end, we establish a close connection between arbitrary SDPs and those arising from the sum-of-squares SDP hierarchy. For approximating maximum constraint satisfaction problems, we prove that SDPs of polynomial-size are equivalent in power to those arising from degree-$O(1)$ sum-of-squares relaxations. This result implies, for instance, that no family of polynomial-size SDP relaxations can achieve better than a 7/8-approximation for MAX-3-SAT.

Connected through hyperedge HE-1 (induced by source paper "lifting with simple gadgets and applications to circuit and proof complexity"):
  with members:
    - "extension complexity of independent set polytopes"
    - "near optimal lower bounds on quantifier depth and weisfeiler leman refinement steps"
    - "approximating rectangles by juntas and weakly exponential lower bounds for lp relaxations of csps"
    - "space proof complexity for random 3 cnfs"
    - "communication lower bounds via critical block sensitivity"

Connected through hyperedge HE-2 (induced by source paper "quantum inspired classical sublinear time algorithm for solving low rank semidefinite programming via sampling approaches"):
  with members:
    - "a quantum inspired classical algorithm for recommendation systems"
    - "fully dynamic maximal independent set with sublinear update time"
    - "approximate nearest neighbor search in high dimensions"
    - "solving linear programs in the current matrix multiplication time"
    - "quantum inspired low rank stochastic regression with logarithmic dependence on the dimension"
    - "quantum inspired sublinear classical algorithms for solving low rank linear systems"
    - "quantum inspired classical algorithms for principal component analysis and supervised clustering"
    - "fully dynamic approximate maximum matching and minimum vertex cover in o log 3 n worst case update time"

Connected through hyperedge HE-3 (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-4 (induced by source paper "extended formulation lower bounds for refuting random csps"):
  with members:
    - "sum of squares lower bounds for refuting any csp"
    - "a nearly tight sum of squares lower bound for the planted clique problem"
    - "approximating rectangles by juntas and weakly exponential lower bounds for lp relaxations of csps"
    - "strongly refuting random csps below the spectral threshold"
    - "how to refute a random csp"
    - "complexity theoretic limitations on learning halfspaces"
    - "tensor prediction rademacher complexity and random 3 xor"
    - "analysis of boolean functions"

Connected through hyperedge HE-5 (induced by source paper "sampling based sublinear low rank matrix arithmetic framework for dequantizing quantum machine learning"):
  with members:
    - "quantum inspired support vector machine"
    - "a quantum inspired classical algorithm for recommendation systems"
    - "a quantum inspired algorithm for general minimum conical hull problems"
    - "quantum inspired classical sublinear time algorithm for solving low rank semidefinite programming via sampling approaches"
    - "a quantum inspired classical algorithm for separable non negative matrix factorization"
    - "quantum inspired low rank stochastic regression with logarithmic dependence on the dimension"
    - "quantum inspired sublinear classical algorithms for solving low rank linear systems"
    - "quantum inspired classical algorithms for principal component analysis and supervised clustering"

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"
    - "sum of squares hierarchy lower bounds for symmetric formulations"
    - "sum of squares proofs and the quest toward optimal algorithms"
    - "approximating csps with global cardinality constraints using sdp hierarchies"

Connected through hyperedge HE-7 (induced by source paper "the power of sum of squares for detecting hidden structures"):
  with members:
    - "exact tensor completion with sum of squares"
    - "a nearly tight sum of squares lower bound for the planted clique problem"
    - "polynomial time tensor decompositions with sum of squares"
    - "multiplicative approximations for polynomial optimization over the unit sphere"
    - "how well do local algorithms solve semidefinite programs"
    - "spectral algorithms for tensor completion"
    - "strongly refuting random csps below the spectral threshold"
    - "improved sum of squares lower bounds for hidden clique and hidden submatrix problems"

Connected through hyperedge HE-8 (induced by source paper "sum of squares lower bounds for refuting any csp"):
  with members:
    - "lower bounds for csp refutation by sdp hierarchies"
    - "the backtracking survey propagation algorithm for solving random k sat problems"
    - "strongly refuting random csps below the spectral threshold"
    - "how to refute a random csp"
    - "noisy tensor completion via the sum of squares hierarchy"
    - "complexity theoretic limitations on learning halfspaces"
    - "sum of squares lower bounds from pairwise independence"
    - "hardness of robust graph isomorphism lasserre gaps and asymmetry of random graphs"

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)