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 algorithms for rectangular matrix multiplication"
  Abstract: Let {\alpha} be the maximal value such that the product of an n x n^{\alpha} matrix by an n^{\alpha} x n matrix can be computed with n^{2+o(1)} arithmetic operations. In this paper we show that \alpha>0.30298, which improves the previous record \alpha>0.29462 by Coppersmith (Journal of Complexity, 1997). More generally, we construct a new algorithm for multiplying an n x n^k matrix by an n^k x n matrix, for any value k\neq 1. The complexity of this algorithm is better than all known algorithms for rectangular matrix multiplication. In the case of square matrix multiplication (i.e., for k=1), we recover exactly the complexity of the algorithm by Coppersmith and Winograd (Journal of Symbolic Computation, 1990). #R##N#These new upper bounds can be used to improve the time complexity of several known algorithms that rely on rectangular matrix multiplication. For example, we directly obtain a O(n^{2.5302})-time algorithm for the all-pairs shortest paths problem over directed graphs with small integer weights, improving over the O(n^{2.575})-time algorithm by Zwick (JACM 2002), and also improve the time complexity of sparse square matrix multiplication.

Connected through hyperedge HE-1 (induced by source paper "deterministic algorithms for decremental approximate shortest paths faster and simpler"):
  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"
    - "dynamic approximate shortest paths and beyond subquadratic and worst case update time"
    - "decremental strongly connected components and single source reachability in near linear time"
    - "algorithms and hardness for diameter in dynamic graphs"
    - "diameter spanner eccentricity spanner and approximating extremal graph distances static dynamic and fault tolerant"
    - "deterministic partially dynamic single source shortest paths in weighted graphs"
    - "thorup zwick emulators are universally optimal hopsets"

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"
    - "limits on the universal method for matrix multiplication"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "efficient construction of behavior graphs for uncertain event data"):
  with members:
    - "discovering process models from uncertain event data"
    - "mining uncertain event data in process mining"
    - "process mining for python pm4py bridging the gap between process and data science"
    - "powers of tensors and fast matrix multiplication"

Connected through hyperedge HE-4 (induced by source paper "faster algorithms for high dimensional robust covariance estimation"):
  with members:
    - "high dimensional robust sparse regression"
    - "efficient algorithms and lower bounds for robust linear regression"
    - "high dimensional robust mean estimation in nearly linear time"
    - "efficient algorithms for outlier robust regression"
    - "sever a robust meta algorithm for stochastic optimization"
    - "learning geometric concepts with nasty noise"
    - "being robust in high dimensions can be practical"
    - "robustly learning a gaussian getting optimal error efficiently"

Connected through hyperedge HE-5 (induced by source paper "approximating apsp without scaling equivalence of approximate min plus and exact min max"):
  with members:
    - "on problems equivalent to min convolution"
    - "subquadratic approximation scheme for partition"
    - "hardness of approximate nearest neighbor search"
    - "on the hardness of approximate and exact bichromatic maximum inner product"
    - "from gap eth to fpt inapproximability clique dominating set and more"
    - "on the fine grained complexity of one dimensional dynamic programming"
    - "distributed pcp theorems for hardness of approximation in p"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"

Connected through hyperedge HE-6 (induced by source paper "mapreduce meets fine grained complexity mapreduce algorithms for apsp matrix multiplication 3 sum and beyond"):
  with members:
    - "approximating edit distance in truly subquadratic time quantum and mapreduce"
    - "truly sub cubic algorithms for language edit distance and rna folding via fast bounded difference min plus product"
    - "random coordinate descent methods for minimizing decomposable submodular functions"
    - "powers of tensors and fast matrix multiplication"
    - "parallel algorithms for geometric graph problems"
    - "access to data and number of iterations dual primal algorithms for maximum matching under resource constraints"
    - "exact weight subgraphs and the k sum conjecture"
    - "communication steps for parallel query processing"

Connected through hyperedge HE-7 (induced by source paper "graph pattern detection hardness for all induced patterns and faster non induced cycles"):
  with members:
    - "graph pattern polynomials"
    - "homomorphisms are a good basis for counting small subgraphs"
    - "tight hardness for shortest cycles and paths in sparse graphs"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "clique based lower bounds for parsing tree adjoining grammars"
    - "finding even cycles faster via capped k walks"
    - "if the current clique algorithms are optimal so is valiant s parser"
    - "powers of tensors and fast matrix multiplication"

Connected through hyperedge HE-8 (induced by source paper "more consequences of falsifying seth and the orthogonal vectors conjecture"):
  with members:
    - "on the fine grained complexity of empirical risk minimization kernel methods and neural networks"
    - "conditional lower bounds for all pairs max flow"
    - "on the fine grained complexity of one dimensional dynamic programming"
    - "distributed pcp theorems for hardness of approximation in p"
    - "tree edit distance cannot be computed in strongly subcubic time unless apsp can"
    - "fine grained complexity of analyzing compressed data quantifying improvements over decompress and solve"
    - "improving viterbi is hard better runtimes imply faster clique algorithms"
    - "on the hardness of partially dynamic graph problems and connections to diameter"

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)