human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "the supermarket model with known and predicted service times"):
  Abstract: The supermarket model typically refers to a system with a large number of queues, where arriving customers choose $d$ queues at random and join the queue with fewest customers. The supermarket model demonstrates the power of even small amounts of choice, as compared to simply joining a queue chosen uniformly at random, for load balancing systems. In this work we perform simulation-based studies to consider variations where service times for a customer are predicted, as might be done in modern settings using machine learning techniques or related mechanisms. To begin, we start by considering the baseline where service times are known. We find that this allows for significant improvements. In particular, not only can the queue being joined be chosen based on the total work at the queue instead of the number of jobs, but also the jobs in the queue can be served using strategies that take advantage of the service times such as shortest job first or shortest remaining processing time. Such strategies greatly improve performance under high load. We then examine the impact of using predictions in place of true service times. Our main takeaway is that using even seemingly weak predictions of service times can yield significant benefits over blind First In First Out queueing in this context. However, some care must be taken when using predicted service time information to both choose a queue and order elements for service within a queue; while in many cases using the information for both choosing and ordering is beneficial, in many of our simulation settings we find that simply using the number of jobs to choose a queue is better when using predicted service times to order jobs in a queue. Our study leaves many natural open questions for further work.

Includes members:
  Member "scheduling with predictions and the price of misprediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online metric algorithms with untrusted predictions")
  Member "a model for learned bloom filters and optimizing by sandwiching":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "online algorithms for multi shop ski rental with machine learned predictions")
  Member "competitive caching with machine learned advice":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "online convex optimization for caching networks")
  Member "on the power of d choices with least loaded server selection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "performance analysis of load balancing policies with memory")
  Member "the case for learned index structures":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "monotonic cardinality estimation of similarity selection a deep learning approach")
  Member "soap one clean analysis of all age based scheduling policies":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "scheduling with predictions and the price of misprediction")

Question: Please tell me which class the source paper 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.PF(Performance)