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: "subcubic equivalences between graph centrality measures and complementary problems"
  Abstract: Despite persistent efforts, there is no known technique for obtaining unconditional super-linear lower bounds for the computational complexity of the problems in P. Vassilevska Williams and Williams introduce a fruitful approach to advance a better understanding of the computational complexity of the problems in P. In particular, they consider All Pairs Shortest Paths (APSP) and other fundamental problems such as checking whether a matrix defines a metric, verifying the correctness of a matrix product, and detecting a negative triangle in a graph. Abboud, Grandoni, and Vassilevska Williams study well-known graph centrality problems such as Radius, Median, etc., and make a connection between their computational complexity to that of two fundamental problems, namely APSP and Diameter. They show any algorithm with subcubic running time for these centrality problems, implies a subcubic algorithm for either APSP or Diameter. In this paper, we define vertex versions for these centrality problems and based on that we introduce new complementary problems. The main open problem of Abboud et al. is whether or not APSP and Diameter are equivalent under subcubic reduction. One of the results of this paper is APSP and CoDiameter, which is the complementary version of Diameter, are equivalent. Moreover, for some of the problems in this set, we show that they are equivalent to their complementary versions. Considering the slight difference between a problem and its complementary version, these equivalences give us the impression that every problem has such a property, and thus APSP and Diameter are equivalent. This paper is a step forward in showing a subcubic equivalence between APSP and Diameter, and we hope that the approach introduced in our paper can be helpful to make this breakthrough happen.

Connected through hyperedge HE-1 (induced by source paper "on the fine grained complexity of parity problems"):
  with members:
    - "approximating subset sum is equivalent to min plus convolution"
    - "on problems equivalent to min convolution"
    - "a subquadratic algorithm for 3xor"
    - "fine grained reductions from approximate counting to decision"
    - "fine grained i o complexity via reductions new lower bounds faster algorithms and a time hierarchy"
    - "on the fine grained complexity of one dimensional dynamic programming"
    - "tight hardness results for maximum weight rectangles"
    - "on problems as hard as cnf sat"

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)