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: "parameterizing pwm and profile matching and knapsack by the feasible weight solutions count"
  Abstract: We study pattern matching on two major representations of uncertain sequences in computational biology---weighted sequences (also known as position weight matrices, PWM) and profiles (i.e., scoring matrices)---in connection with a multichoice generalization of the classic knapsack problem. We propose algorithms for profile matching and multichoice knapsack parameterized by the number of solutions with feasible weight. In the worst case, our algorithms run as fast as the best known non-parameterized solutions for these problems. For multichoice knapsack, this proves that our dependence on the parameter is optimal up to lower-order terms conditioned on the optimality of the state-of-the-art algorithms for Subset Sum and 3-Sum problems. We apply our algorithms to derive efficient solutions for the classic and general version of weighted pattern matching---in the former only the text is weighted, whereas in the latter both the pattern and the text can be weighted. We also argue that the resulting algorithm for general weighted pattern matching inherits the optimality results from multichoice knapsack.

Connected through hyperedge HE-1 (induced by source paper "seth based lower bounds for subset sum and bicriteria path"):
  with members:
    - "on the fine grained complexity of empirical risk minimization kernel methods and neural networks"
    - "a short note on merlin arthur protocols for subset sum"
    - "subquadratic algorithms for algebraic generalizations of 3sum"
    - "a near linear pseudopolynomial time algorithm for subset sum"
    - "a dichotomy for regular expression membership testing"
    - "improved bounds for 3sum k sum and linear degeneracy"
    - "clustered integer 3sum via additive combinatorics"
    - "which regular expression patterns are hard to match"

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)