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: "compact trip representation over networks"
  Abstract: We present a new Compact Trip Representation (\(\mathsf {CTR}\)) that allows us to manage users’ trips (moving objects) over networks. These could be public transportation networks (buses, subway, trains, and so on) where nodes are stations or stops, or road networks where nodes are intersections. \(\mathsf {CTR}\) represents the sequences of nodes and time instants in users’ trips. The spatial component is handled with a data structure based on the well-known Compressed Suffix Array (\(\mathsf {CSA}\)), which provides both a compact representation and interesting indexing capabilities. We also represent the temporal component of the trips, that is, the time instants when users visit nodes in their trips. We create a sequence with these time instants, which are then self-indexed with a balanced Wavelet Matrix (\(\mathsf {WM}\)). This gives us the ability to solve range-interval queries efficiently. We show how \(\mathsf {CTR}\) can solve relevant spatial and spatio-temporal queries over large sets of trajectories. Finally, we also provide experimental results to show the space requirements and query efficiency of \(\mathsf {CTR}\).

Connected through hyperedge HE-1 (induced by source paper "about birds project bioinformatics and information retrieval data structures analysis and design"):
  with members:
    - "compact representations of event sequences"
    - "parallel construction of wavelet trees on multicore architectures"
    - "run compressed rank select for large alphabets"
    - "efficient compression and indexing of trajectories"
    - "grammar compressed sequences with rank select support"
    - "compressed representation of dynamic binary relations with applications"
    - "a succinct data structure for self indexing ternary relations"
    - "aggregated 2d range queries on clustered points"

Connected through hyperedge HE-2 (induced by source paper "a compact representation for trips over networks built on self indexes"):
  with members:
    - "cinct compression and retrieval for massive vehicular trajectories via relative movement labeling"
    - "efficient and compact representations of some non canonical prefix free codes"
    - "on compressing permutations and adaptive sorting"
    - "new algorithms on wavelet trees and applications to information retrieval"
    - "compressed text indexes from theory to practice"
    - "practical entropy compressed rank select dictionary"

Connected through hyperedge HE-3 (induced by source paper "faster and smaller two level index for network based trajectories"):
  with members:
    - "cinct compression and retrieval for massive vehicular trajectories via relative movement labeling"
    - "efficient compression and indexing of trajectories"
    - "gract a grammar based compressed representation of trajectories"
    - "composite repetition aware data structures"
    - "from theory to practice plug and play with succinct data structures"
    - "practical entropy compressed rank select dictionary"

Connected through hyperedge HE-4 (induced by source paper "using compressed suffix arrays for a compact representation of temporal graphs"):
  with members:
    - "action2activity recognizing complex activities from sensor data"
    - "an introduction to temporal graphs an algorithmic perspective"
    - "exploring the structure and function of temporal networks with dynamic graphlets"
    - "windows into relational events data structures for contiguous subsequences of edges"
    - "efficient snapshot retrieval over historical graph data"
    - "succinct indexable dictionaries with applications to encoding k ary trees prefix sums and multisets"

Connected through hyperedge HE-5 (induced by source paper "cinct compression and retrieval for massive vehicular trajectories via relative movement labeling"):
  with members:
    - "csa fast pattern search for large alphabets"
    - "press a novel framework of trajectory compression in road networks"
    - "on compressing permutations and adaptive sorting"
    - "fixed block compression boosting in fm indexes"

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)