human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "dimension expanders via rank condensers"):
  Abstract: An emerging theory of "linear-algebraic pseudorandomness" aims to understand the linear-algebraic analogs of fundamental Boolean pseudorandom objects where the rank of subspaces plays the role of the size of subsets. In this work, we study and highlight the interrelationships between several such algebraic objects such as subspace designs, dimension expanders, seeded rank condensers, two-source rank condensers, and rank-metric codes. In particular, with the recent construction of near-optimal subspace designs by Guruswami and Kopparty as a starting point, we construct good (seeded) rank condensers (both lossless and lossy versions), which are a small collection of linear maps $\mathbb{F}^n \to \mathbb{F}^t$ for $t \ll n$ such that for every subset of $\mathbb{F}^n$ of small rank, its rank is preserved (up to a constant factor in the lossy case) by at least one of the maps. #R##N#We then compose a tensoring operation with our lossy rank condenser to construct constant-degree dimension expanders over polynomially large fields. That is, we give $O(1)$ explicit linear maps $A_i:\mathbb{F}^n\to \mathbb{F}^n$ such that for any subspace $V \subseteq \mathbb{F}^n$ of dimension at most $n/2$, $\dim\bigl( \sum_i A_i(V)\bigr) \ge (1+\Omega(1)) \dim(V)$. Previous constructions of such constant-degree dimension expanders were based on Kazhdan's property $T$ (for the case when $\mathbb{F}$ has characteristic zero) or monotone expanders (for every field $\mathbb{F}$); in either case the construction was harder than that of usual vertex expanders. Our construction, on the other hand, is simpler. #R##N#Via an equivalence to linear rank-metric codes, we then construct optimal lossless two-source condensers. We then use our seeded rank condensers to obtain near-optimal lossy two-source condensers for constant rank sources.

Includes members:
  Member "deterministic truncation of linear matroids":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on quasipolynomial multicut mimicking networks and kernelization of multiway cut problems")
  Member "explicit rank metric codes list decodable with optimal redundancy":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "row reduction applied to decoding of rank metric and subspace codes")
  Member "pseudorandomness for multilinear read once algebraic branching programs in any order":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "progress on polynomial identity testing ii")
  Member "folded codes from function field towers and improved optimal rate list decoding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "linear time erasure list decoding of expander codes")
  Member "on identity testing of tensors low rank recovery and compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "lower bounds for matrix factorization")
  Member "quasi polynomial hitting set for set depth delta formulas":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "succinct hitting sets and barriers to proving algebraic circuits lower bounds")
  Member "fast matrix rank algorithms and applications":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "elimination based certificates for triangular equivalence and rank profiles")
  Member "linear algebraic list decoding of folded reed solomon codes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "beyond the guruswami sudan and parvaresh vardy radii folded reed solomon multiplicity and derivative codes")

Question: Please tell me which class the source paper 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)