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: "limits on the universal method for matrix multiplication"
  Abstract: In a recent work, Alman and Vassilevska Williams [FOCS 2018, arXiv:1810.08671 [cs.CC]] proved limitations on designing matrix multiplication algorithms using the Galactic method applied to many tensors of interest, including the family of Coppersmith-Winograd tensors. In this note, we extend all their lower bounds to the more powerful Universal method, which can use any degeneration of a tensor instead of just monomial degenerations. Our main result is that the Universal method applied to any Coppersmith-Winograd tensor cannot yield a bound on $\omega$ better than $2.16805$. We also study a slightly restricted form of Strassen's Laser method and prove that it is optimal: when it applies to a tensor $T$, it achieves $\omega=2$ if and only if it is possible for the Universal method applied to $T$ to achieve $\omega=2$.

Connected through hyperedge HE-1 (induced by source paper "new algorithms and hardness for incremental single source shortest paths in directed graphs"):
  with members:
    - "decremental sssp in weighted digraphs faster and against an adaptive adversary"
    - "fully dynamic all pairs shortest paths improved worst case time and space bounds"
    - "deterministic algorithms for decremental approximate shortest paths faster and simpler"
    - "decremental strongly connected components and single source reachability in near linear time"
    - "graph pattern detection hardness for all induced patterns and faster non induced cycles"
    - "limits on all known and some unknown approaches to matrix multiplication"
    - "diameter spanners eccentricity spanners and approximating extremal distances"
    - "algorithms and hardness for diameter in dynamic graphs"

Connected through hyperedge HE-2 (induced by source paper "barriers for rectangular matrix multiplication"):
  with members:
    - "solving empirical risk minimization in the current matrix multiplication time"
    - "limits on all known and some unknown approaches to matrix multiplication"
    - "solving linear programs in the current matrix multiplication time"
    - "barriers for fast matrix multiplication from irreversibility"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "further limitations of the known approaches for matrix multiplication"
    - "fast matrix multiplication limitations of the laser method"
    - "powers of tensors and fast matrix multiplication"

Connected through hyperedge HE-3 (induced by source paper "geometric rank of tensors and subrank of matrix multiplication"):
  with members:
    - "variety membership testing algebraic natural proofs and geometric complexity theory"
    - "limits on all known and some unknown approaches to matrix multiplication"
    - "on multilinear forms bias correlation and tensor rank"
    - "barriers for fast matrix multiplication from irreversibility"
    - "bias vs structure of polynomials in large fields and applications in effective algebraic geometry and coding theory"
    - "fast matrix multiplication limitations of the laser method"
    - "powers of tensors and fast matrix multiplication"
    - "variety evasive sets"

Connected through hyperedge HE-4 (induced by source paper "solving empirical risk minimization in the current matrix multiplication time"):
  with members:
    - "nearly sample optimal sparse fourier transform in any dimension ripless and filterless"
    - "limits on all known and some unknown approaches to matrix multiplication"
    - "solving linear programs in the current matrix multiplication time"
    - "on the local minima of the empirical risk"
    - "guaranteed sufficient decrease for variance reduced stochastic gradient descent"
    - "empirical risk minimization for stochastic convex optimization o 1 n and o 1 n 2 type of risk bounds"
    - "variance reduced stochastic gradient descent with sufficient decrease"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"

Connected through hyperedge HE-5 (induced by source paper "barriers for fast matrix multiplication from irreversibility"):
  with members:
    - "limits on all known and some unknown approaches to matrix multiplication"

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)