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: "a prg for lipschitz functions of polynomials with applications to sparsest cut"
  Abstract: We give improved pseudorandom generators (PRGs) for Lipschitz functions of low-degree polynomials over the hypercube. These are functions of the form psi(P(x)), where P is a low-degree polynomial and psi is a function with small Lipschitz constant. PRGs for smooth functions of low-degree polynomials have received a lot of attention recently and play an important role in constructing PRGs for the natural class of polynomial threshold functions. In spite of the recent progress, no nontrivial PRGs were known for fooling Lipschitz functions of degree O(log n) polynomials even for constant error rate. In this work, we give the first such generator obtaining a seed-length of (log n)\tilde{O}(d^2/eps^2) for fooling degree d polynomials with error eps. Previous generators had an exponential dependence on the degree. #R##N#We use our PRG to get better integrality gap instances for sparsest cut, a fundamental problem in graph theory with many applications in graph optimization. We give an instance of uniform sparsest cut for which a powerful semi-definite relaxation (SDP) first introduced by Goemans and Linial and studied in the seminal work of Arora, Rao and Vazirani has an integrality gap of exp(\Omega((log log n)^{1/2})). Understanding the performance of the Goemans-Linial SDP for uniform sparsest cut is an important open problem in approximation algorithms and metric embeddings and our work gives a near-exponential improvement over previous lower bounds which achieved a gap of \Omega(log log n).

Connected through hyperedge HE-1 (induced by source paper "approximating sparsest cut in low rank graphs via embeddings from approximately low dimensional spaces"):
  with members:
    - "guruswami sinop rounding without higher level lasserre"
    - "improved arv rounding in small set expanders and graphs of bounded threshold rank"
    - "improved cheeger s inequality analysis of spectral partitioning algorithms through higher order spectral gap"
    - "approximating non uniform sparsest cut via generalized spectra"

Connected through hyperedge HE-2 (induced by source paper "derandomized graph product results using the low degree long code"):
  with members:
    - "super polylogarithmic hypergraph coloring hardness via low degree long codes"
    - "reducing uniformity in khot saket hypergraph coloring hardness reductions"
    - "making the long code shorter with applications to the unique games conjecture"
    - "bounded independence fools halfspaces"
    - "pseudorandom generators for polynomial threshold functions"
    - "conditional hardness for approximate coloring"

Connected through hyperedge HE-3 (induced by source paper "polynomial bounds for decoupling with applications"):
  with members:
    - "beating the random assignment on constraint satisfaction problems of bounded degree"
    - "analysis of boolean functions"
    - "k independent gaussians fool polynomial threshold functions"

Connected through hyperedge HE-4 (induced by source paper "locally testable codes and cayley graphs"):
  with members:
    - "making the long code shorter with applications to the unique games conjecture"
    - "some applications of coding theory in computational complexity"

Connected through hyperedge HE-5 (induced by source paper "constant factor lasserre integrality gaps for graph partitioning problems"):
  with members:
    - "hypercontractivity sum of squares proofs and their applications"
    - "majority is stablest discrete and sos"
    - "approximability and proof complexity"
    - "polynomial integrality gaps for strong sdp relaxations of densest k subgraph"
    - "reductions between expansion 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)