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: "beyond trace reconstruction population recovery from the deletion channel"
  Abstract: \emph{Population recovery} is the problem of learning an unknown distribution over an unknown set of $n$-bit strings, given access to independent draws from the distribution that have been independently corrupted according to some noise channel. Recent work has intensively studied such problems both for the bit-flip and erasure noise channels. #R##N#We initiate the study of population recovery under the \emph{deletion channel}, in which each bit is independently \emph{deleted} with some fixed probability and the surviving bits are concatenated and transmitted. This is a far more challenging noise model than bit-flip~noise or erasure noise; indeed, even the simplest case in which the population size is 1 (corresponding to a trivial distribution supported on a single string) corresponds to the \emph{trace reconstruction} problem, a challenging problem that has received much recent attention (see e.g.~\cite{DOS17,NP17,PZ17,HPP18,HHP18}). #R##N#We give algorithms and lower bounds for population recovery under the deletion channel when the population size is some $\ell>1$. As our main sample complexity upper bound, we show that for any $\ell=o(\log n/\log \log n)$, a population of $\ell$ strings from $\{0,1\}^n$ can be learned under deletion channel noise using $\smash{2^{n^{1/2+o(1)}}}$ samples. On the lower bound side, we show that $n^{\Omega(\ell)}$ samples are required to perform population recovery under the deletion channel, for all $\ell \leq n^{1/2-\epsilon}$. #R##N#Our upper bounds are obtained via a robust multivariate generalization of a polynomial-based analysis, due to Krasikov and Roddity \cite{KR97}, of how the $k$-deck of a bit-string uniquely identifies the string; this is a very different approach from recent algorithms for trace reconstruction (the $\ell=1$ case). Our lower bounds build on moment-matching results of Roos~\cite{Roo00} and Daskalakis and Papadimitriou~\cite{DP15}.

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"
    - "reconstructing trees from traces"
    - "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 "coded trace reconstruction in a constant number of traces"):
  with members:
    - "coding for deletion channels with multiple traces"
    - "coded trace reconstruction"
    - "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"
    - "synchronization strings explicit constructions local decoding and applications"

Connected through hyperedge HE-3 (induced by source paper "trace reconstruction generalized and parameterized"):
  with members:
    - "coded trace reconstruction"
    - "reconstructing trees from traces"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "efficient average case population recovery in the presence of insertions and deletions"):
  with members:
    - "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"
    - "optimal mean based algorithms for trace reconstruction"
    - "a polynomial time algorithm for lossy population recovery"

Connected through hyperedge HE-5 (induced by source paper "reconstructing trees from traces"):
  with members:
    - "trace reconstruction generalized and parameterized"
    - "optimal mean based algorithms for trace reconstruction"
    - "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.DS(Data Structures and Algorithms)