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: "faster sdp hierarchy solvers for local rounding algorithms"
  Abstract: Convex relaxations based on different hierarchies of linear/semi-definite programs have been used recently to devise approximation algorithms for various optimization problems. The approximation guarantee of these algorithms improves with the number of {\em rounds} $r$ in the hierarchy, though the complexity of solving (or even writing down the solution for) the $r$'th level program grows as $n^{\Omega(r)}$ where $n$ is the input size. #R##N#In this work, we observe that many of these algorithms are based on {\em local} rounding procedures that only use a small part of the SDP solution (of size $n^{O(1)} 2^{O(r)}$ instead of $n^{\Omega(r)}$). We give an algorithm to find the requisite portion in time polynomial in its size. The challenge in achieving this is that the required portion of the solution is not fixed a priori but depends on other parts of the solution, sometimes in a complicated iterative manner. #R##N#Our solver leads to $n^{O(1)} 2^{O(r)}$ time algorithms to obtain the same guarantees in many cases as the earlier $n^{O(r)}$ time algorithms based on $r$ rounds of the Lasserre hierarchy. In particular, guarantees based on $O(\log n)$ rounds can be realized in polynomial time. #R##N#We develop and describe our algorithm in a fairly general abstract framework. The main technical tool in our work, which might be of independent interest in convex optimization, is an efficient ellipsoid algorithm based separation oracle for convex programs that can output a {\em certificate of infeasibility with restricted support}. This is used in a recursive manner to find a sequence of consistent points in nested convex bodies that "fools" local rounding algorithms.

Connected through hyperedge HE-1 (induced by source paper "approximating constraint satisfaction problems on high dimensional expanders"):
  with members:
    - "agreement testing theorems on layered set systems"
    - "good distance lattices from high dimensional expanders"
    - "log concave polynomials ii high dimensional walks and an fpras for counting bases of a matroid"
    - "sum of squares lower bounds for refuting any csp"
    - "high dimensional random walks and colorful expansion"
    - "a birthday repetition theorem and complexity of approximating dense csps"
    - "a new regularity lemma and faster approximation algorithms for low threshold rank graphs"
    - "rounding semidefinite programming hierarchies via global correlation"

Connected through hyperedge HE-2 (induced by source paper "matrix decompositions and sparse graph regularity"):
  with members:
    - "a new regularity lemma and faster approximation algorithms for low threshold rank graphs"
    - "rounding semidefinite programming hierarchies via global correlation"
    - "approximating non uniform sparsest cut via generalized spectra"

Connected through hyperedge HE-3 (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-4 (induced by source paper "guruswami sinop rounding without higher level lasserre"):
  with members:
    - "rounding lasserre sdps using column selection and spectrum based approximation schemes for graph partitioning and quadratic ips"
    - "towards a better approximation for sparsest cut"
    - "approximating non uniform sparsest cut via generalized spectra"
    - "optimal column based low rank matrix reconstruction"
    - "a log n omega 1 integrality gap for the sparsest cut sdp"

Connected through hyperedge HE-5 (induced by source paper "rounding lasserre sdps using column selection and spectrum based approximation schemes for graph partitioning and quadratic ips"):
  with members:
    - "hypercontractivity sum of squares proofs and their applications"
    - "majority is stablest discrete and sos"
    - "approximability and proof complexity"
    - "rounding semidefinite programming hierarchies via global correlation"
    - "approximating non uniform sparsest cut via generalized spectra"
    - "optimal column based low rank matrix reconstruction"
    - "spectral algorithms for unique games"
    - "near optimal column based matrix reconstruction"

Connected through hyperedge HE-6 (induced by source paper "improved cheeger s inequality analysis of spectral partitioning algorithms through higher order spectral gap"):
  with members:
    - "clustering is difficult only when it does not matter"
    - "on the practically interesting instances of maxcut"
    - "many sparse cuts via higher eigenvalues"
    - "approximating the expansion profile and almost optimal local graph clustering"
    - "approximating non uniform sparsest cut via generalized spectra"
    - "empirical comparison of algorithms for network community detection"
    - "are stable instances easy"
    - "eigenvalue bounds spectral partitioning and metrical deformations via flows"

Connected through hyperedge HE-7 (induced by source paper "towards a better approximation for sparsest cut"):
  with members:
    - "improved arv rounding in small set expanders and graphs of bounded threshold rank"
    - "many sparse cuts via higher eigenvalues"
    - "hypercontractivity sum of squares proofs and their applications"
    - "approximability and proof complexity"
    - "rounding semidefinite programming hierarchies via global correlation"
    - "min max graph partitioning and small set expansion"
    - "optimal column based low rank matrix reconstruction"
    - "efficient volume sampling for row column subset selection"

Connected through hyperedge HE-8 (induced by source paper "a new regularity lemma and faster approximation algorithms for low threshold rank graphs"):
  with members:
    - "rounding semidefinite programming hierarchies via global correlation"
    - "approximating non uniform sparsest cut via generalized spectra"
    - "spectral algorithms for unique games"

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.DS(Data Structures and Algorithms)