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: "hypercontractivity sum of squares proofs and their applications"
  Abstract: We study the computational complexity of approximating the 2-to-q norm of linear operators (defined as |A|2->q = maxv≠ 0|Av|q/|v|2) for q > 2, as well as connections between this question and issues arising in quantum information theory and the study of Khot's Unique Games Conjecture (UGC). We show the following: For any constant even integer q ≥ 4, a graph G is a small-set expander if and only if the projector into the span of the top eigenvectors of G's adjacency matrix has bounded 2->q norm. As a corollary, a good approximation to the 2->q norm will refute the Small-Set Expansion Conjecture --- a close variant of the UGC. We also show that such a good approximation can be obtained in exp(n2/q) time, thus obtaining a different proof of the known subexponential algorithm for Small-Set-Expansion. Constant rounds of the "Sum of Squares" semidefinite programing hierarchy certify an upper bound on the 2->4 norm of the projector to low degree polynomials over the Boolean cube, as well certify the unsatisfiability of the "noisy cube" and "short code" based instances of Unique-Games considered by prior works. This improves on the previous upper bound of exp(logO(1) n) rounds (for the "short code"), as well as separates the "Sum of Squares"/"Lasserre" hierarchy from weaker hierarchies that were known to require ω(1) rounds. We show reductions between computing the 2->4 norm and computing the injective tensor norm of a tensor, a problem with connections to quantum information theory. Three corollaries are: (i) the 2->4 norm is NP-hard to approximate to precision inverse-polynomial in the dimension, (ii) the 2->4 norm does not have a good approximation (in the sense above) unless 3-SAT can be solved in time exp(√n poly log(n)), and (iii) known algorithms for the quantum separability problem imply a non-trivial additive approximation for the 2->4 norm.

Connected through hyperedge HE-1 (induced by source paper "sum of squares bounds for the ordering principle"):
  with members:
    - "sum of squares lower bounds from symmetry and a good story"
    - "sum of squares proofs and the quest toward optimal algorithms"

Connected through hyperedge HE-2 (induced by source paper "how hard is robust mean estimation"):
  with members:
    - "adversarial examples from cryptographic pseudo random generators"
    - "being robust in high dimensions can be practical"
    - "mixture models robustness and sum of squares proofs"
    - "resilience a criterion for learning in the presence of arbitrary outliers"
    - "learning from untrusted data"
    - "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures"
    - "agnostic estimation of mean and covariance"
    - "robust estimators in high dimensions without the computational intractability"

Connected through hyperedge HE-3 (induced by source paper "approximate guarantees for dictionary learning"):
  with members:
    - "towards learning sparsely used dictionaries with arbitrary supports"
    - "sparse approximation via generating point sets"
    - "on the computational intractability of exact and approximate dictionary learning"
    - "variable selection is hard"
    - "sparse and spurious dictionary learning with noise and outliers"
    - "dictionary learning and tensor decomposition via the sum of squares method"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "exact recovery of sparsely used dictionaries"

Connected through hyperedge HE-4 (induced by source paper "semi algebraic proofs ips lower bounds and the tau conjecture can a natural number be negative"):
  with members:
    - "proof complexity lower bounds from algebraic circuit complexity"
    - "algebraic proof complexity progress frontiers and challenges"
    - "characterizing propositional proofs as non commutative formulas"
    - "communication lower bounds via critical block sensitivity"
    - "approximability and proof complexity"

Connected through hyperedge HE-5 (induced by source paper "sum of squares meets nash optimal lower bounds for finding any equilibrium"):
  with members:
    - "better agnostic clustering via relaxed tensor norms"
    - "sum of squares lower bounds for refuting any csp"
    - "strongly refuting random csps below the spectral threshold"
    - "how to refute a random csp"
    - "approximability and proof complexity"
    - "settling the complexity of computing two player nash equilibria"
    - "games of fixed rank a hierarchy of bimatrix games"

Connected through hyperedge HE-6 (induced by source paper "polynomial time probabilistic reasoning with partial observations via implicit learning in probability logics"):
  with members:
    - "bayesian estimation from few samples community detection and related problems"
    - "provable learning of noisy or networks"
    - "tensor principal component analysis via sum of squares proofs"
    - "rounding sum of squares relaxations"
    - "from statistical knowledge bases to degrees of belief"
    - "characterizing and reasoning about probabilistic and non probabilistic expectation"

Connected through hyperedge HE-7 (induced by source paper "mildly exponential time approximation algorithms for vertex cover uniform sparsest cut and related problems"):
  with members:
    - "sum of squares lower bounds for refuting any csp"
    - "sum of squares proofs and the quest toward optimal algorithms"
    - "approximability and proof complexity"
    - "reductions between expansion problems"

Connected through hyperedge HE-8 (induced by source paper "inapproximability of matrix p rightarrow q norms"):
  with members:
    - "hypercontractivity and its applications"

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)