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: "approximate sparse linear regression"
  Abstract: In the Sparse Linear Regression (SLR) problem, given a $d \times n$ matrix $M$ and a $d$-dimensional vector $q$, we want to compute a $k$-sparse vector $\tau$ such that the error $||M \tau-q||$ is minimized. In this paper, we present algorithms and conditional lower bounds for several variants of this problem. In particular, we consider (i) the Affine SLR where we add the constraint that $\sum_i \tau_i=1$ and (ii) the Convex SLR where we further add the constraint that $\tau \geq 0$. Furthermore, we consider (i) the batched (offline) setting, where the matrix $M$ and the vector $q$ are given as inputs in advance, and (ii) the query(online) setting, where an algorithm preprocesses the matrix $M$ to quickly answer such queries. All of the aforementioned variants have been well-studied and have many applications in statistics, machine learning and sparse recovery. #R##N#We consider the approximate variants of these problems in the "low sparsity regime" where the value of the sparsity bound $k$ is low. In particular, we show that the online variant of all three problems can be solved with query time $\tilde O(n^{k-1})$. This provides non-trivial improvements over the naive algorithm that exhaustively searches all ${ n \choose k}$ subsets $B$. We also show that solving the offline variant of all three problems, would require an exponential dependence of the form $\tilde \Omega(n^{k/2}/e^{k})$, under a natural complexity-theoretic conjecture. Improving this lower bound for the case of $k=4$ would imply a nontrivial lower bound for the famous Hopcroft's problem. Moreover, solving the offline variant of affine SLR in $o(n^{k-1})$ would imply an upper bound of $o(n^d)$ for the problem of testing whether a given set of $n$ points in a $d$-dimensional space is degenerate. However, this is conjectured to require $\Omega(n^d)$ time.

Connected through hyperedge HE-1 (induced by source paper "sparse regression via range counting"):
  with members:
    - "online sparse linear regression"
    - "variable selection is hard"
    - "orthogonal range searching on the ram revisited"

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.CG(Computational Geometry)