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: "the wavelet trie maintaining an indexed sequence of strings in compressed space"
  Abstract: An indexed sequence of strings is a data structure for storing a string sequence that supports random access, searching, range counting and analytics operations, both for exact matches and prefix search. String sequences lie at the core of column-oriented databases, log processing, and other storage and query tasks. In these applications each string can appear several times and the order of the strings in the sequence is relevant. The prefix structure of the strings is relevant as well: common prefixes are sought in strings to extract interesting features from the sequence. Moreover, space-efficiency is highly desirable as it translates directly into higher performance, since more data can fit in fast memory. #R##N#We introduce and study the problem of compressed indexed sequence of strings, representing indexed sequences of strings in nearly-optimal compressed space, both in the static and dynamic settings, while preserving provably good performance for the supported operations. #R##N#We present a new data structure for this problem, the Wavelet Trie, which combines the classical Patricia Trie with the Wavelet Tree, a succinct data structure for storing a compressed sequence. The resulting Wavelet Trie smoothly adapts to a sequence of strings that changes over time. It improves on the state-of-the-art compressed data structures by supporting a dynamic alphabet (i.e. the set of distinct strings) and prefix queries, both crucial requirements in the aforementioned applications, and on traditional indexes by reducing space occupancy to close to the entropy of the sequence.

Connected through hyperedge HE-1 (induced by source paper "the case for learned index structures"):
  with members:
    - "a model for learned bloom filters and related structures"
    - "a tree a bounded approximate index structure"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "m bonsai a practical compact dynamic trie"):
  with members:
    - "from theory to practice plug and play with succinct data structures"
    - "one variable word equations in linear time"
    - "cram compressed random access memory"

Connected through hyperedge HE-3 (induced by source paper "improved space efficient linear time algorithms for some classical graph problems"):
  with members:
    - "biconnectivity st numbering and other applications of dfs using o n bits"
    - "a simple test on 2 vertex and 2 edge connectivity"

Connected through hyperedge HE-4 (induced by source paper "parallel wavelet tree construction"):
  with members:
    - "parallelism in randomized incremental algorithms"
    - "parallel local graph clustering"
    - "sorting with asymmetric read and write costs"
    - "efficient algorithms with asymmetric read and write costs"
    - "wavelet trees meet suffix trees"
    - "counting triangles in massive graphs with mapreduce"
    - "parallel graph decompositions using random shifts"
    - "skew strikes back new developments in the theory of join algorithms"

Connected through hyperedge HE-5 (induced by source paper "wavelet trees meet suffix trees"):
  with members:
    - "weighted ancestors in suffix trees"
    - "sorted range reporting revisited"
    - "internal pattern matching queries in a text and applications"

Connected through hyperedge HE-6 (induced by source paper "waterfowl a compact self indexed rdf store with inference enabled dictionaries"):
  with members:
    - "rewriting ontological queries into small nonrecursive datalog programs"

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)