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 computational complexity of finding temporal paths under waiting time constraints"
  Abstract: Computing a (shortest) path between two vertices in a graph is one of the most fundamental primitive in graph algorithmics. In recent years, the study of paths in temporal graphs, that is, graphs where the vertex set remains static but the edge set may change over time, gained more and more attention. In a nutshell, temporal paths have to respect time, that is, they may only move forward in time. More formally, the time edges used by a temporal path either need to have increasing or non-decreasing time stamps. In is well known that computing temporal paths is polynomial-time solvable. We study a natural variant, where temporal paths may only dwell a certain given amount of time steps in any vertex, which we call restless temporal paths. This small modification creates a significant change in the computational complexity of the task of finding temporal paths. We show that finding restless temporal paths is NP-complete and give a thorough analysis of the (parameterized) computational complexity of this problem. In particular, we show that problem remains computationally hard on temporal graphs with three layers and is W[1]-hard when parameterized by the feedback vertex number of the underlying graph. On the positive side, we give an efficient (FPT) algorithm to find short restless temporal paths that has an asymptotically optimal running time assuming the Exponential Time Hypothesis.

Connected through hyperedge HE-1 (induced by source paper "finding temporal patterns using algebraic fingerprints"):
  with members:
    - "approximately counting and sampling small witnesses using a colourful decision oracle"
    - "motivo fast motif counting via succinct color coding and adaptive sampling"
    - "mining frequent patterns in evolving graphs"
    - "motifs in temporal networks"
    - "a motif based approach for identifying controversy"
    - "stream graphs and link streams for the modeling of interactions over time"
    - "higher order organization of complex networks"
    - "on the complexity of rainbow coloring problems"

Connected through hyperedge HE-2 (induced by source paper "efficient computation of optimal temporal walks under waiting time constraints"):
  with members:
    - "multistage s t path confronting similarity with dissimilarity"
    - "the complexity of finding small separators in temporal graphs"
    - "on the size and the approximability of minimum temporally connected subgraphs"
    - "temporal network optimization subject to connectivity constraints"
    - "route planning in transportation networks"
    - "time varying graphs and dynamic networks"

Connected through hyperedge HE-3 (induced by source paper "comparing temporal graphs using dynamic time warping"):
  with members:
    - "dynamic time warping in strongly subquadratic time algorithms for the low distance regime and approximate evaluation"
    - "temporal matching"
    - "multistage problems on a global budget"
    - "computing maximum matchings in temporal graphs"
    - "fast exact dynamic time warping on run length encoded time series"
    - "multi net a scalable multiplex network embedding framework"
    - "alignment of dynamic networks"
    - "the complexity of finding small separators in temporal graphs"

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.DM(Discrete Mathematics)