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: "trace reconstruction generalized and parameterized"
  Abstract: In the beautifully simple-to-state problem of trace reconstruction, the goal is to reconstruct an unknown binary string $x$ given random "traces" of $x$ where each trace is generated by deleting each coordinate of $x$ independently with probability $p<1$. The problem is well studied both when the unknown string is arbitrary and when it is chosen uniformly at random. For both settings, there is still an exponential gap between upper and lower sample complexity bounds and our understanding of the problem is still surprisingly limited. In this paper, we consider natural parameterizations and generalizations of this problem in an effort to attain a deeper and more comprehensive understanding. #R##N#We prove that $\exp(O(n^{1/4} \sqrt{\log n}))$ traces suffice for reconstructing arbitrary matrices. In the matrix version of the problem, each row and column of an unknown $\sqrt{n}\times \sqrt{n}$ matrix is deleted independently with probability $p$. Our results contrasts with the best known results for sequence reconstruction where the best known upper bound is $\exp(O(n^{1/3}))$. An optimal result for random matrix reconstruction: we show that $\Theta(\log n)$ traces are necessary and sufficient. This is in contrast to the problem for random sequences where there is a super-logarithmic lower bound and the best known upper bound is $\exp({O}(\log^{1/3} n))$. We show that $\exp(O(k^{1/3}\log^{2/3} n))$ traces suffice to reconstruct $k$-sparse strings, providing an improvement over the best known sequence reconstruction results when $k = o(n/\log^2 n)$. We show that $\textrm{poly}(n)$ traces suffice if $x$ is $k$-sparse and we additionally have a "separation" promise, specifically that the indices of 1's in $x$ all differ by $\Omega(k \log n)$.

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"
    - "beyond trace reconstruction population recovery from the deletion channel"
    - "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"
    - "beyond trace reconstruction population recovery from the deletion channel"
    - "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 "efficient average case population recovery in the presence of insertions and deletions"):
  with members:
    - "beyond trace reconstruction population recovery from the deletion channel"
    - "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-4 (induced by source paper "sample complexity of learning mixtures of sparse linear regressions"):
  with members:
    - "learning mixtures of sparse linear regressions using sparse graph codes"
    - "solving a mixture of many random linear equations by tensor decomposition and alternating minimization"
    - "tight bounds for learning a mixture of two gaussians"
    - "spectral experts for estimating mixtures of linear regressions"
    - "settling the polynomial learnability of mixtures of gaussians"

Connected through hyperedge HE-5 (induced by source paper "reconstructing trees from traces"):
  with members:
    - "beyond trace reconstruction population recovery from the deletion channel"
    - "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)