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: "variable selection is hard"
  Abstract: Variable selection for sparse linear regression is the problem of finding, given an m x p matrix B and a target vector y, a sparse vector x such that Bx approximately equals y. Assuming a standard complexity hypothesis, we show that no polynomial-time algorithm can find a k'-sparse x with ||Bx-y||^2 0, C_1>0,C_2>0 are arbitrary. This is true even under the promise that there is an unknown k-sparse vector x^* satisfying Bx^*=y. We prove a similar result for a statistical version of the problem in which the data are corrupted by noise. #R##N#To the authors' knowledge, these are the first hardness results for sparse regression that apply when the algorithm simultaneously has k'>k and h(m,p)>0.

Connected through hyperedge HE-1 (induced by source paper "approximate guarantees for dictionary learning"):
  with members:
    - "towards learning sparsely used dictionaries with arbitrary supports"
    - "sparse approximation via generating point sets"
    - "on the computational intractability of exact and approximate dictionary learning"
    - "sparse and spurious dictionary learning with noise and outliers"
    - "dictionary learning and tensor decomposition via the sum of squares method"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "hypercontractivity sum of squares proofs and their applications"
    - "exact recovery of sparsely used dictionaries"

Connected through hyperedge HE-2 (induced by source paper "sparse regression via range counting"):
  with members:
    - "approximate sparse linear regression"
    - "online sparse linear regression"
    - "orthogonal range searching on the ram revisited"

Connected through hyperedge HE-3 (induced by source paper "minimal reachability is hard to approximate"):
  with members:
    - "minimal inputs outputs for a networked system"
    - "minimal reachability problems"
    - "minimizing convergence error in multi agent systems via leader selection a supermodular optimization approach"
    - "controllability metrics limitations and algorithms for complex networks"
    - "optimal approximation for submodular and supermodular optimization with bounded curvature"
    - "a supermodular optimization framework for leader selection under link noise in linear multi agent systems"

Connected through hyperedge HE-4 (induced by source paper "adaptive feature selection computationally efficient online sparse linear regression under rip"):
  with members:
    - "online sparse linear regression"
    - "greedy minimization of weakly supermodular set functions"
    - "learning without concentration"
    - "linear regression with limited observation"
    - "efficient learning with partially observed attributes"

Connected through hyperedge HE-5 (induced by source paper "ensemble representation learning an analysis of fitness and survival for wrapper based genetic programming methods"):
  with members:
    - "symbolic regression algorithms with built in linear regression"
    - "epsilon lexicase selection for regression"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-6 (induced by source paper "online sparse linear regression"):
  with members:
    - "analytical approach to parallel repetition"
    - "linear regression with limited observation"
    - "efficient learning with partially observed attributes"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-7 (induced by source paper "optimal sparse linear auto encoders and sparse pca"):
  with members:
    - "optimal cur matrix decompositions"
    - "near optimal column based matrix reconstruction"

Connected through hyperedge HE-8 (induced by source paper "np hardness and inapproximability of sparse pca"):
  with members:
    - "optimal solutions for sparse principal component analysis"
    - "a direct formulation for sparse pca using semidefinite programming"

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)