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: "tight lower bounds for query processing on streaming and external memory data"
  Abstract: We study a clean machine model for external memory and stream processing. We show that the number of scans of the external data induces a strict hierarchy (as long as work space is sufficiently small, e.g., polylogarithmic in the size of the input). We also show that neither joins nor sorting are feasible if the product of the number $r(n)$ of scans of the external memory and the size $s(n)$ of the internal memory buffers is sufficiently small, e.g., of size $o(\sqrt[5]{n})$. We also establish tight bounds for the complexity of XPath evaluation and filtering.

Connected through hyperedge HE-1 (induced by source paper "streamability of nested word transductions"):
  with members:
    - "randomized sliding window algorithms for regular languages"
    - "untwisting two way transducers in elementary time"
    - "two way visibly pushdown automata and transducers"
    - "streaming property testing of visibly pushdown languages"
    - "from two way to one way finite state transducers"
    - "streaming tree transducers"
    - "on functionality of visibly pushdown transducers"
    - "validating xml documents in the streaming model with external memory"

Connected through hyperedge HE-2 (induced by source paper "unidirectional input output streaming complexity of reversal and sorting"):
  with members:
    - "on the value of multiple read write streams for data compression"
    - "streaming complexity of checking priority queues"
    - "data oblivious external memory algorithms for the compaction selection and sorting of outsourced data"
    - "information cost tradeoffs for augmented index and streaming language recognition"
    - "validating xml documents in the streaming model with external memory"
    - "the space complexity of recognizing well parenthesized expressions in the streaming model the index function revisited"
    - "recognizing well parenthesized expressions in the streaming model"
    - "reversal complexity revisited"

Connected through hyperedge HE-3 (induced by source paper "beyond worst case analysis for joins with minesweeper"):
  with members:
    - "size bounds and query plans for relational joins"
    - "instance optimal geometric algorithms"
    - "leapfrog triejoin a worst case optimal join algorithm"
    - "efficient triangle counting in large graphs via degree based vertex partitioning"
    - "self improving algorithms"
    - "hypertree decompositions and tractable queries"
    - "optimal aggregation algorithms for middleware"

Connected through hyperedge HE-4 (induced by source paper "on the value of multiple read write streams for data compression"):
  with members:
    - "grammar based compression in a streaming model"
    - "lightweight data indexing and compression in external memory"
    - "reversal complexity revisited"
    - "large alphabets and incompressibility"

Connected through hyperedge HE-5 (induced by source paper "validating xml documents in the streaming model with external memory"):
  with members:
    - "the space complexity of recognizing well parenthesized expressions in the streaming model the index function revisited"
    - "recognizing well parenthesized expressions in the streaming model"
    - "randomized computations on large data sets tight lower bounds"

Connected through hyperedge HE-6 (induced by source paper "new algorithms and lower bounds for sequential access data compression"):
  with members:
    - "on optimally partitioning a text to improve its compression"
    - "low memory adaptive prefix coding"
    - "dynamic asymmetric communication"
    - "reversal complexity revisited"
    - "large alphabets and incompressibility"
    - "dynamic shannon coding"

Connected through hyperedge HE-7 (induced by source paper "lower bounds for multi pass processing of multiple data streams"):
  with members:
    - "randomized computations on large data sets tight lower bounds"

Connected through hyperedge HE-8 (induced by source paper "ams without 4 wise independence on product domains"):
  with members:
    - "measuring independence of datasets"

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)