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: "hitting sets for low distance multilinear depth 3"
  Abstract: The depth-$3$ model has recently gained much importance, as it has become a stepping-stone to understanding general arithmetic circuits. Its restriction to multilinearity has known exponential lower bounds but no nontrivial blackbox identity tests. In this paper we take a step towards designing such hitting-sets. We define a notion of distance for multilinear depth-$3$ circuits (say, in $n$ variables and $k$ product gates) that measures how far are the partitions from a mere refinement. The $1$-distance strictly subsumes the set-multilinear model, while $n$-distance captures general multilinear depth-$3$. We design a hitting-set in time poly($n^{\delta\log k}$) for $\delta$-distance. Further, we give an extension of our result to models where the distance is large (close to $n$) but it is small when restricted to certain variables. This implies the first subexponential whitebox PIT for the sum of constantly many set-multilinear depth-$3$ circuits. #R##N#We also explore a new model of read-once algebraic branching programs (ROABP) where the factor-matrices are invertible (called invertible-factor ROABP). We design a hitting-set in time poly($\text{size}^{w^2}$) for width-$w$ invertible-factor ROABP. Further, we could do without the invertibility restriction when $w=2$. Previously, the best result for width-$2$ ROABP was quasi-polynomial time (Forbes-Saptharishi-Shpilka, arXiv 2013). #R##N#The common thread in all these results is the phenomenon of low-support `rank concentration'. We exploit the structure of these models to prove rank-concentration after a `small shift' in the variables. Our proof techniques are stronger than the results of Agrawal-Saha-Saxena (STOC 2013) and Forbes-Saptharishi-Shpilka (arXiv 2013); giving us quasi-polynomial-time hitting-sets for models where no subexponential whitebox algorithms were known before.

Connected through hyperedge HE-1 (induced by source paper "progress on polynomial identity testing ii"):
  with members:
    - "pseudorandomness for multilinear read once algebraic branching programs in any order"
    - "improved bounds for reduction to depth 4 and depth 3"
    - "explicit noether normalization for simultaneous conjugation via polynomial identity testing"
    - "quasipolynomial time identity testing of non commutative and read once oblivious algebraic branching programs"
    - "algebraic independence in positive characteristic a p adic calculus"
    - "on identity testing of tensors low rank recovery and compressed sensing"
    - "geometric complexity theory v equivalence between blackbox derandomization of polynomial identity testing and derandomization of noether s normalization lemma"
    - "jacobian hits circuits hitting sets lower bounds for depth d occur k formulas depth 3 transcendence degree k circuits"

Connected through hyperedge HE-2 (induced by source paper "hitting sets for roabp and sum of set multilinear circuits"):
  with members:
    - "progress on polynomial identity testing ii"
    - "subexponential size hitting sets for bounded depth multilinear formulas"
    - "pseudorandomness and fourier growth bounds for width 3 branching programs"
    - "pseudorandomness for multilinear read once algebraic branching programs in any order"
    - "pseudorandomness for regular branching programs via fourier analysis"
    - "quasipolynomial time identity testing of non commutative and read once oblivious algebraic branching programs"
    - "on identity testing of tensors low rank recovery and compressed sensing"
    - "geometric complexity theory v equivalence between blackbox derandomization of polynomial identity testing and derandomization of noether s normalization lemma"

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)