human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "comparing temporal graphs using dynamic time warping"):
  Abstract: The connections within many real-world networks change over time. Thus, there has been a recent boom in studying temporal graphs. Recognizing patterns in temporal graphs requires a similarity measure to compare different temporal graphs. To this end, we initiate the study of dynamic time warping (an established concept for mining time series data) on temporal graphs. We propose the dynamic temporal graph warping distance (dtgw) to determine the (dis-)similarity of two temporal graphs. Our novel measure is flexible and can be applied in various application domains. We show that computing the dtgw-distance is a challenging (NP-hard) optimization problem and identify some polynomial-time solvable special cases. Moreover, we develop a quadratic programming formulation and an efficient heuristic. Preliminary experiments indicate that the heuristic performs very well and that our concept yields meaningful results on real-world instances.

Includes members:
  Member "dynamic time warping in strongly subquadratic time algorithms for the low distance regime and approximate evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "streaming with oracle new streaming algorithms for edit distance and lcs")
  Member "temporal matching":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multistage graph problems on a global budget")
  Member "multistage problems on a global budget":
    also connected through other hyperedges:
      (none)
  Member "computing maximum matchings in temporal graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multistage graph problems on a global budget")
  Member "the computational complexity of finding temporal paths under waiting time constraints":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "finding temporal patterns using algebraic fingerprints")
  Member "fast exact dynamic time warping on run length encoded time series":
    also connected through other hyperedges:
      (none)
  Member "multi net a scalable multiplex network embedding framework":
    also connected through other hyperedges:
      (none)
  Member "alignment of dynamic networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dna dynamic social network alignment")

Question: Please tell me which class the source paper 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.LG(Machine Learning)