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: "reconstructing trees from traces"
  Abstract: We study the problem of learning a node-labeled tree given independent traces from an appropriately defined deletion channel. This problem, tree trace reconstruction, generalizes string trace reconstruction, which corresponds to the tree being a path. For many classes of trees, including complete trees and spiders, we provide algorithms that reconstruct the labels using only a polynomial number of traces. This exhibits a stark contrast to known results on string trace reconstruction, which require exponentially many traces, and where a central open problem is to determine whether a polynomial number of traces suffice. Our techniques combine novel combinatorial and complex analytic methods.

Connected through hyperedge HE-1 (induced by source paper "coded trace reconstruction"):
  with members:
    - "capacity results for the noisy shuffling channel"
    - "anchor based correction of substitutions in indexed sets"
    - "clustering correcting codes"
    - "coding for deletion channels with multiple traces"
    - "beyond trace reconstruction population recovery from the deletion channel"
    - "trace reconstruction generalized and parameterized"
    - "sequence subset distance and coding for error control in dna based data storage"
    - "deterministic document exchange protocols and almost optimal binary codes for edit errors"

Connected through hyperedge HE-2 (induced by source paper "trace reconstruction generalized and parameterized"):
  with members:
    - "coded trace reconstruction"
    - "beyond trace reconstruction population recovery from the deletion channel"
    - "mixture models robustness and sum of squares proofs"
    - "tight bounds for learning a mixture of two gaussians"
    - "learning mixtures of structured distributions over discrete domains"
    - "settling the polynomial learnability of mixtures of gaussians"
    - "polynomial learning of distribution families"

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.CC(Computational Complexity)