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: "approximation algorithms for the incremental knapsack problem via disjunctive programming"
  Abstract: In the incremental knapsack problem (IK), we are given a knapsack whose capacity grows weakly as a function of time. There is a time horizon of T periods and the capacity of the knapsack is Bt in period t for t = 1,...,T. We are also given a set S of N items to be placed in the knapsack. Item i has a value of vi and a weight of wi that is independent of the time period. At any time period t, the sum of the weights of the items in the knapsack cannot exceed the knapsack capac- ity Bt. Moreover, once an item is placed in the knapsack, it cannot be removed from the knapsack at a later time period. We seek to maximize the sum of (discounted) knapsack values over time subject to the capac- ity constraints. We first give a constant factor approximation algorithm for IK, under mild restrictions on the growth rate of Bt (the constant factor depends on the growth rate). We then give a PTAS for IIK, the special case of IK with no discounting, when T = O( p log N).

Connected through hyperedge HE-1 (induced by source paper "deeppr progressive recovery for interdependent vnfs with deep reinforcement learning"):
  with members:
    - "deeppr incremental recovery for interdependent vnfs with deep reinforcement learning"
    - "error correction coding meets cyber physical systems message passing analysis of self healing interdependent networks"

Connected through hyperedge HE-2 (induced by source paper "deeppr incremental recovery for interdependent vnfs with deep reinforcement learning"):
  with members:
    - "error correction coding meets cyber physical systems message passing analysis of self healing interdependent networks"

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)