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: "association schemes non commutative polynomial concentration and sum of squares lower bounds for planted clique"
  Abstract: Finding cliques in random graphs and the closely related "planted" clique variant, where a clique of size t is planted in a random G(n,1/2) graph, have been the focus of substantial study in algorithm design. Despite much effort, the best known polynomial-time algorithms only solve the problem for t = Theta(sqrt(n)). Here we show that beating sqrt(n) would require substantially new algorithmic ideas, by proving a lower bound for the problem in the sum-of-squares (or Lasserre) hierarchy, the most powerful class of semi-definite programming algorithms we know of: r rounds of the sum-of-squares hierarchy can only solve the planted clique for t > sqrt(n)/(C log n)^(r^2). Previously, no nontrivial lower bounds were known. Our proof is formulated as a degree lower bound in the Positivstellensatz algebraic proof system, which is equivalent to the sum-of-squares hierarchy. The heart of our (average-case) lower bound is a proof that a certain random matrix derived from the input graph is (with high probability) positive semidefinite. Two ingredients play an important role in this proof. The first is the classical theory of association schemes, applied to the average and variance of that random matrix. The second is a new large deviation inequality for matrix-valued polynomials. Our new tail estimate seems to be of independent interest and may find other applications, as it generalizes both the estimates on real-valued polynomials and on sums of independent random matrices.

Connected through hyperedge HE-1 (induced by source paper "fast spectral algorithms from sum of squares proofs tensor decomposition and planted sparse vectors"):
  with members:
    - "finding a sparse vector in a subspace linear sparsity using alternating directions"
    - "a lasserre based 1 varepsilon approximation for pm mid p_j 1 textrm prec mid c_ max"
    - "learning mixtures of gaussians in high dimensions"
    - "sum of squares lower bounds for sparse pca"
    - "decomposing overcomplete 3rd order tensors using sum of squares algorithms"
    - "tensor principal component analysis via sum of squares proofs"
    - "tensor prediction rademacher complexity and random 3 xor"
    - "powers of tensors and fast matrix multiplication"

Connected through hyperedge HE-2 (induced by source paper "speeding up sum of squares for tensor decomposition and planted sparse vectors"):
  with members:
    - "finding a sparse vector in a subspace linear sparsity using alternating directions"
    - "learning mixtures of gaussians in high dimensions"
    - "sum of squares lower bounds for sparse pca"
    - "decomposing overcomplete 3rd order tensors using sum of squares algorithms"
    - "tensor principal component analysis via sum of squares proofs"
    - "tensor prediction rademacher complexity and random 3 xor"
    - "a statistical model for tensor pca"
    - "sum of squares proofs and the quest toward optimal algorithms"

Connected through hyperedge HE-3 (induced by source paper "sum of squares lower bounds for planted clique"):
  with members:
    - "improved sum of squares lower bounds for hidden clique and hidden submatrix problems"
    - "hypercontractivity sum of squares proofs and their applications"
    - "approximability and proof complexity"
    - "rounding semidefinite programming hierarchies via global correlation"
    - "polynomial integrality gaps for strong sdp relaxations of densest k subgraph"

Connected through hyperedge HE-4 (induced by source paper "improved sum of squares lower bounds for hidden clique and hidden submatrix problems"):
  with members:
    - "sum of squares proofs and the quest toward optimal algorithms"
    - "simultaneously structured models with application to sparse and low rank matrices"
    - "statistical algorithms and a lower bound for detecting planted clique"
    - "nuclear norm minimization for the planted clique and biclique 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.CC(Computational Complexity)