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: "a tree a bounded approximate index structure"
  Abstract: Index structures are one of the most important tools that DBAs leverage in order to improve the performance of analytics and transactional workloads. However, with the explosion of data that is constantly being generated in a wide variety of domains including autonomous vehicles, Internet of Things (IoT) devices, and E-commerce sites, building several indexes can often become prohibitive and consume valuable system resources. In fact, a recent study has shown that indexes created as part of the TPC-C benchmark can account for 55% of the total memory available in a state-of-the-art in-memory DBMS. This overhead consumes valuable and expensive main memory, and limits the amount of space that a database has available to store new data or process existing data. #R##N#In this paper, we present a novel approximate index structure called A-Tree. At the core of our index is a tunable error parameter that allows a DBA to balance lookup performance and space consumption. To navigate this tradeoff, we provide a cost model that helps the DBA choose an appropriate error parameter given either (1) a lookup latency requirement (e.g., 500ns) or (2) a storage budget (e.g., 100MB). Using a variety of real-world datasets, we show that our index structure is able to provide performance that is comparable to full index structures while reducing the storage footprint by orders of magnitude.

Connected through hyperedge HE-1 (induced by source paper "alex an updatable adaptive learned index"):
  with members:
    - "learned cardinalities estimating correlated joins with deep learning"
    - "learning state representations for query optimization with deep reinforcement learning"
    - "learning to optimize join queries with deep reinforcement learning"
    - "learning memory access patterns"
    - "towards a hands free query optimizer through deep learning"
    - "the case for learned index structures"
    - "insertion sort is o n log n"

Connected through hyperedge HE-2 (induced by source paper "a scalable learned index scheme in storage systems"):
  with members:
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "the case for learned index structures"
    - "very deep convolutional networks for large scale image recognition"
    - "flat combining and the synchronization parallelism tradeoff"

Connected through hyperedge HE-3 (induced by source paper "learning multi dimensional indexes"):
  with members:
    - "alex an updatable adaptive learned index"
    - "quicksel quick selectivity learning with mixture models"
    - "the case for learned index structures"

Connected through hyperedge HE-4 (induced by source paper "superseding traditional indexes by orchestrating learning and geometry"):
  with members:
    - "the case for learned index structures"
    - "a comprehensive survey on fog computing state of the art and research challenges"
    - "bicriteria data compression"

Connected through hyperedge HE-5 (induced by source paper "massivizing computer systems a vision to understand design and engineer computer ecosystems through and beyond modern distributed systems"):
  with members:
    - "weld rethinking the interface between data intensive applications"
    - "self awareness of cloud applications"
    - "ready for rain a view from spec research on the future of cloud metrics"
    - "what next a dozen information technology research goals"

Connected through hyperedge HE-6 (induced by source paper "the case for learned index structures"):
  with members:
    - "a model for learned bloom filters and related structures"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "imagenet training in minutes"
    - "the end of a myth distributed transactions can scale"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "tensorflow a system for large scale machine learning"
    - "learning to hash for indexing big data a survey"
    - "monotonic calibrated interpolated look up tables"

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.DB(Databases)