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: "certifying random polynomials over the unit sphere via sum of squares hierarchy"
  Abstract: Given a random n-variate degree-d homogeneous polynomial f , we study the following problem:

Connected through hyperedge HE-1 (induced by source paper "community detection in hypergraphs spiked tensor models and sum of squares"):
  with members:
    - "community recovery in hypergraphs"
    - "semidefinite programs on sparse random graphs and their application to community detection"
    - "tensor principal component analysis via sum of squares proofs"
    - "a statistical model for tensor pca"
    - "exact recovery in the stochastic block model"
    - "sum of squares proofs and the quest toward optimal algorithms"
    - "community detection thresholds and the weak ramanujan property"

Connected through hyperedge HE-2 (induced by source paper "strongly refuting random csps below the spectral threshold"):
  with members:
    - "how to refute a random csp"
    - "complexity theoretic limitations on learning halfspaces"
    - "tensor principal component analysis via sum of squares proofs"
    - "tensor prediction rademacher complexity and random 3 xor"
    - "fast spectral algorithms from sum of squares proofs tensor decomposition and planted sparse vectors"
    - "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates"
    - "dictionary learning and tensor decomposition via the sum of squares method"
    - "a statistical model for tensor pca"

Connected through hyperedge HE-3 (induced by source paper "weak decoupling polynomial folds and approximate optimization over the sphere"):
  with members:
    - "the power of sum of squares for detecting hidden structures"
    - "sum of squares lower bounds for refuting any csp"
    - "a nearly tight sum of squares lower bound for the planted clique problem"
    - "sum of squares certificates for maxima of random tensors on the sphere"
    - "multiplicative approximations for polynomial optimization over the unit sphere"
    - "strongly refuting random csps below the spectral threshold"
    - "decomposing overcomplete 3rd order tensors using sum of squares algorithms"
    - "dictionary learning and tensor decomposition via the sum of squares method"

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)