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: "online stochastic bin packing"
  Abstract: Motivated by the problem of packing Virtual Machines on physical servers in the cloud, we study the problem of one-dimensional online stochastic bin packing. Items with sizes i.i.d. from an unknown distribution with integral support arrive as a stream and must be packed on arrival in bins of size B, also an integer. The size of an item is known when it arrives and the goal is to minimize the waste, defined to be the total unused space in non-empty bins. While there are many heuristics for online stochastic bin packing, all such heuristics are either optimal for only certain classes of item size distributions, or rely on learning the distribution. The state-of-the-art Sum of Squares heuristic (Csirik et al.) obtains sublinear (in number of items seen) waste for distributions where the expected waste for the optimal offline algorithm is sublinear, but has a constant factor larger waste for distributions with linear waste under OPT. Csirik et al. solved this problem by learning the distribution and solving an LP to inject phantom jobs in the arrival stream. #R##N#We present two distribution-agnostic bin packing heuristics that achieve additive O(sqrt{n}) waste compared to OPT for all distributions. Our algorithms are gradient descent on suitably defined Lagrangian relaxations of the bin packing Linear Program. The first algorithm is very similar to the SS algorithm, but conceptually packs the bins top-down instead of bottom-up. This motivates our second heuristic that uses a different Lagrangian relaxation to pack bins bottom-up. #R##N#Next, we consider the more general problem of online stochastic bin packing with item departures where the time requirement of an item is only revealed when the item departs. Our algorithms extend as is to the case of item departures. We also briefly revisit the Best Fit heuristic which has not been studied in the scenario of item departures yet.

Connected through hyperedge HE-1 (induced by source paper "orl reinforcement learning benchmarks for online stochastic optimization problems"):
  with members:
    - "learning dexterous in hand manipulation"
    - "distributed prioritized experience replay"
    - "efficient large scale fleet management via multi agent deep reinforcement learning"
    - "rllib abstractions for distributed reinforcement learning"
    - "learning combinatorial optimization algorithms over graphs"
    - "a deep q network for the beer game a deep reinforcement learning algorithm to solve inventory optimization problems"
    - "proximal policy optimization algorithms"
    - "neural combinatorial optimization with reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "overcommitment in cloud services bin packing with chance constraints"):
  with members:
    - "submodular approximation sampling based algorithms and lower bounds"

Connected through hyperedge HE-3 (induced by source paper "first come first served for online slot allocation and huffman coding"):
  with members:
    - "huffman coding with letter costs a linear time approximation scheme"
    - "on the sum of squares algorithm for bin packing"
    - "on line paging against adversarially biased random inputs"

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)