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: "dynamic matrix inverse improved algorithms and matching conditional lower bounds"
  Abstract: The dynamic matrix inverse problem is to maintain the inverse of a matrix undergoing element and column updates. It is the main subroutine behind the best algorithms for many dynamic problems whose complexity is not yet well-understood, such as maintaining the largest eigenvalue, rank and determinant of a matrix and maintaining reachability, distances, maximum matching size, and $k$-paths/cycles in a graph. Understanding the complexity of dynamic matrix inverse is a key to understand these problems. #R##N#In this paper, we present (i) improved algorithms for dynamic matrix inverse and their extensions to some incremental/look-ahead variants, and (ii) variants of the Online Matrix-Vector conjecture [Henzinger et al. STOC'15] that, if true, imply that these algorithms are tight. Our algorithms automatically lead to faster dynamic algorithms for the aforementioned problems, some of which are also tight under our conjectures, e.g. reachability and maximum matching size (closing the gaps for these two problems was in fact asked by Abboud and V. Williams [FOCS'14]). Prior best bounds for most of these problems date back to more than a decade ago [Sankowski FOCS'04, COCOON'05, SODA'07; Kavitha FSTTCS'08; Mucha and Sankowski Algorithmica'10; Bosek et al. FOCS'14]. #R##N#Our improvements stem mostly from the ability to use fast matrix multiplication ``one more time'', to maintain a certain transformation matrix which could be maintained only combinatorially previously (i.e. without fast matrix multiplication). Oddly, unlike other dynamic problems where this approach, once successful, could be repeated several times (``bootstrapping''), our conjectures imply that this is not the case for dynamic matrix inverse and some related problems. However, when a small additional ``look-ahead'' information is provided we can perform such repetition to drive the bounds down further.

Connected through hyperedge HE-1 (induced by source paper "solving tall dense linear programs in nearly linear time"):
  with members:
    - "solving empirical risk minimization in the current matrix multiplication time"
    - "dynamic streaming spectral sparsification in nearly linear time and space"
    - "fully dynamic spectral vertex sparsifiers and applications"
    - "a deterministic linear program solver in current matrix multiplication time"
    - "solving linear programs in the current matrix multiplication time"
    - "relative error tensor low rank approximation"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "optimality of the johnson lindenstrauss lemma"

Connected through hyperedge HE-2 (induced by source paper "faster fully dynamic transitive closure in practice"):
  with members:
    - "fully dynamic single source reachability in practice an experimental study"
    - "snap a general purpose network analysis and graph mining library"
    - "unifying and strengthening hardness for dynamic problems via the online matrix vector multiplication conjecture"
    - "mantaining dynamic matrices for fully dynamic transitive closure"

Connected through hyperedge HE-3 (induced by source paper "dynamic approximate shortest paths and beyond subquadratic and worst case update time"):
  with members:
    - "fully dynamic all pairs shortest paths improved worst case time and space bounds"
    - "sensitive distance and reachability oracles for large batch updates"
    - "towards tight approximation bounds for graph diameter and eccentricities"
    - "algorithms and hardness for diameter in dynamic graphs"
    - "diameter spanner eccentricity spanner and approximating extremal graph distances static dynamic and fault tolerant"
    - "a deamortization approach for dynamic spanner and dynamic maximal matching"
    - "deterministic partially dynamic single source shortest paths in weighted graphs"
    - "fully dynamic approximate maximum matching and minimum vertex cover in o log 3 n worst case update time"

Connected through hyperedge HE-4 (induced by source paper "a new deterministic algorithm for dynamic set cover"):
  with members:
    - "breaking quadratic time for small vertex connectivity and an approximation scheme"
    - "a new algorithm for decremental single source shortest paths with applications to vertex capacitated flow and cut problems"
    - "a deamortization approach for dynamic spanner and dynamic maximal matching"
    - "fully dynamic approximate maximum matching and minimum vertex cover in o log 3 n worst case update time"
    - "dynamic minimum spanning forest with subpolynomial worst case update time"
    - "deterministic fully dynamic approximate vertex cover and fractional matching in o 1 amortized update time"
    - "fully dynamic maximal matching in constant update time"
    - "new deterministic approximation algorithms for fully dynamic matching"

Connected through hyperedge HE-5 (induced by source paper "a deterministic linear program solver in current matrix multiplication time"):
  with members:
    - "solving empirical risk minimization in the current matrix multiplication time"
    - "distributed edge connectivity in sublinear time"
    - "limits on all known and some unknown approaches to matrix multiplication"
    - "solving linear programs in the current matrix multiplication time"
    - "derandomization beyond connectivity undirected laplacian systems in nearly logarithmic space"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "powers of tensors and fast matrix multiplication"
    - "path finding i solving linear programs with o sqrt rank linear system solves"

Connected through hyperedge HE-6 (induced by source paper "sensitive distance and reachability oracles for large batch updates"):
  with members:
    - "deterministic combinatorial replacement paths and distance sensitivity oracles"
    - "dynamic approximate shortest paths and beyond subquadratic and worst case update time"
    - "conditional hardness for sensitivity problems"
    - "fast deterministic computation of the hermite normal form and determinant of a polynomial matrix"
    - "connectivity oracles for graphs subject to vertex failures"
    - "incremental and fully dynamic subgraph connectivity for emergency planning"
    - "powers of tensors and fast matrix multiplication"
    - "popular conjectures imply strong lower bounds for dynamic 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.DS(Data Structures and Algorithms)