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: "hardness of consensus problems for circular strings and time series averaging"
  Abstract: Consensus problems for strings and sequences appear in numerous application contexts, ranging from bioinformatics over data mining to speech recognition. Closing some gaps in the literature, we show that some fundamental problems in this context are NP-hard, W[1]-hard, and the known (partially brute-force) exact algorithms are essentially close to optimality assuming the Exponential Time Hypothesis. Our classified problems include the Circular Consensus String problem, whose non-circular version is trivially linear-time solvable. We introduce a much more general family of circular consensus string problems that also serves as a key for our hardness reductions, and proves to be of independent (also practical) interest on its own. In particular, our work answers the main open question concerning the computational complexity of exact mean computation in dynamic time warping spaces [Brill et al., SDM 2018].

Connected through hyperedge HE-1 (induced by source paper "an average compress algorithm for the sample mean problem under dynamic time warping"):
  with members:
    - "nonsmooth analysis and subgradient methods for averaging in dynamic time warping spaces"
    - "exact mean computation in dynamic time warping spaces"
    - "asymmetric learning vector quantization for efficient nearest neighbor classification in dynamic time warping spaces"
    - "on the existence of a sample mean in dynamic time warping spaces"
    - "adam a method for stochastic optimization"
    - "on the relation between the common labelling and the median graph"

Connected through hyperedge HE-2 (induced by source paper "making the dynamic time warping distance warping invariant"):
  with members:
    - "the ucr time series archive"
    - "nonsmooth analysis and subgradient methods for averaging in dynamic time warping spaces"
    - "exact mean computation in dynamic time warping spaces"
    - "asymmetric learning vector quantization for efficient nearest neighbor classification in dynamic time warping spaces"
    - "on the existence of a sample mean in dynamic time warping spaces"
    - "faster retrieval with a two pass dynamic time warping lower bound"

Connected through hyperedge HE-3 (induced by source paper "on the hardness of computing an average curve"):
  with members:
    - "tight hardness results for consensus problems on circular strings and time series"
    - "approximating k median via pseudo approximation"

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.DM(Discrete Mathematics)