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 "bisect and conquer hierarchical clustering via max uncut bisection"):
  Abstract: Hierarchical Clustering is an unsupervised data analysis method which has been widely used for decades. Despite its popularity, it had an underdeveloped analytical foundation and to address this, Dasgupta recently introduced an optimization viewpoint of hierarchical clustering with pairwise similarity information that spurred a line of work shedding light on old algorithms (e.g., Average-Linkage), but also designing new algorithms. Here, for the maximization dual of Dasgupta's objective (introduced by Moseley-Wang), we present polynomial-time .4246 approximation algorithms that use Max-Uncut Bisection as a subroutine. The previous best worst-case approximation factor in polynomial time was .336, improving only slightly over Average-Linkage which achieves 1/3. Finally, we complement our positive results by providing APX-hardness (even for 0-1 similarities), under the Small Set Expansion hypothesis.

Includes members:
  Member "hierarchical clustering better than average linkage":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "supervised hierarchical clustering with exponential linkage")
  Member "hierarchical clustering for euclidean data":
    also connected through other hyperedges:
      (none)
  Member "hierarchical clustering with structural constraints":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep discriminative clustering analysis")
  Member "adaptive hierarchical clustering using ordinal queries":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "online hierarchical clustering approximations")
  Member "hierarchical clustering objective functions and algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "compact representation of uncertainty in hierarchical clustering")
  Member "massively parallel algorithms and hardness for single linkage clustering under ell_p distances":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "interactive bayesian hierarchical clustering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "diameter based interactive structure discovery")
  Member "hierarchical clustering via spreading metrics":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hierarchical correlation clustering and tree preserving embedding")

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.DS(Data Structures and Algorithms)