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: "optimal mean based algorithms for trace reconstruction"
  Abstract: In the (deletion-channel) trace reconstruction problem, there is an unknown $n$-bit source string $x$. An algorithm is given access to independent traces of $x$, where a trace is formed by deleting each bit of~$x$ independently with probability~$\delta$. The goal of the algorithm is to recover~$x$ exactly (with high probability), while minimizing samples (number of traces) and running time. #R##N#Previously, the best known algorithm for the trace reconstruction problem was due to Holenstein~et~al.; it uses $\exp(\tilde{O}(n^{1/2}))$ samples and running time for any fixed $0 < \delta < 1$. It is also what we call a "mean-based algorithm", meaning that it only uses the empirical means of the individual bits of the traces. Holenstein~et~al.~also gave a lower bound, showing that any mean-based algorithm must use at least $n^{\tilde{\Omega}(\log n)}$ samples. #R##N#In this paper we improve both of these results, obtaining matching upper and lower bounds for mean-based trace reconstruction. For any constant deletion rate $0 < \delta < 1$, we give a mean-based algorithm that uses $\exp(O(n^{1/3}))$ time and traces; we also prove that any mean-based algorithm must use at least $\exp(\Omega(n^{1/3}))$ traces. In fact, we obtain matching upper and lower bounds even for $\delta$ subconstant and $\rho := 1-\delta$ subconstant: when $(\log^3 n)/n \ll \delta \leq 1/2$ the bound is $\exp(-\Theta(\delta n)^{1/3})$, and when $1/\sqrt{n} \ll \rho \leq 1/2$ the bound is $\exp(-\Theta(n/\rho)^{1/3})$. #R##N#Our proofs involve estimates for the maxima of Littlewood polynomials on complex disks. We show that these techniques can also be used to perform trace reconstruction with random insertions and bit-flips in addition to deletions. We also find a surprising result: for deletion probabilities $\delta > 1/2$, the presence of insertions can actually help with trace reconstruction.

Connected through hyperedge HE-1 (induced by source paper "the error probability of maximum likelihood decoding over two deletion channels"):
  with members:
    - "coding for sequence reconstruction for single edits"
    - "tutorial on algebraic deletion correction codes"
    - "coding for deletion channels with multiple traces"
    - "from clustering supersequences to entropy minimizing subsequences for single and double deletions"
    - "average case reconstruction for the deletion channel subpolynomially many traces suffice"
    - "coding for racetrack memories"
    - "codes correcting a burst of deletions or insertions"
    - "fundamental bounds and approaches to sequence reconstruction from nanopore sequencers"

Connected through hyperedge HE-2 (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"
    - "reconstructing trees from 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"

Connected through hyperedge HE-3 (induced by source paper "coded trace reconstruction in a constant number of traces"):
  with members:
    - "coding for deletion channels with multiple traces"
    - "coded trace reconstruction"
    - "beyond trace reconstruction population recovery from the deletion channel"
    - "trace reconstruction generalized and parameterized"
    - "unique reconstruction of coded strings from multiset substring spectra"
    - "capacity upper bounds for deletion type channels"
    - "average case reconstruction for the deletion channel subpolynomially many traces suffice"
    - "polynomial time decodable codes for the binary deletion channel"

Connected through hyperedge HE-4 (induced by source paper "efficient average case population recovery in the presence of insertions and deletions"):
  with members:
    - "beyond trace reconstruction population recovery from the deletion channel"
    - "trace reconstruction generalized and parameterized"
    - "sharp bounds for population recovery"
    - "average case reconstruction for the deletion channel subpolynomially many traces suffice"
    - "noisy population recovery in polynomial time"
    - "a polynomial time algorithm for lossy population recovery"

Connected through hyperedge HE-5 (induced by source paper "beyond trace reconstruction population recovery from the deletion channel"):
  with members:
    - "sharp bounds for population recovery"
    - "average case reconstruction for the deletion channel subpolynomially many traces suffice"
    - "noisy population recovery in polynomial time"
    - "a polynomial time algorithm for lossy population recovery"

Connected through hyperedge HE-6 (induced by source paper "reconstructing trees from traces"):
  with members:
    - "beyond trace reconstruction population recovery from the deletion channel"
    - "trace reconstruction generalized and parameterized"
    - "trace complexity of network inference"

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)