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: "approximating matrix p norms"
  Abstract: We consider the problem of computing the q->p norm of a matrix A, which is defined for p,q \ge 1, as |A|_{q->p} = max_{x !=0 } |Ax|_p / |x|_q. This is in general a non-convex optimization problem, and is a natural generalization of the well-studied question of computing singular values (this corresponds to p=q=2). Different settings of parameters give rise to a variety of known interesting problems (such as the Grothendieck problem when p=1 and q=\infty). However, very little is understood about the approximability of the problem for different values of p,q. Our first result is an efficient algorithm for computing the q->p norm of matrices with non-negative entries, when q \ge p \ge 1. The algorithm we analyze is based on a natural fixed point iteration, which can be seen as an analog of power iteration for computing eigenvalues. We then present an application of our techniques to the problem of constructing a scheme for oblivious routing in the l_p norm. This makes constructive a recent existential result of Englert and R\"acke [ER] on O(log n)-competitive oblivious routing schemes (which they make constructive only for p=2). On the other hand, when we do not have any restrictions on the entries (such as non-negativity), we prove that the problem is NP-hard to approximate to any constant factor, for 2  p cannot be approximated to a factor 2^{(log n)^{1-eps}}, for any \eps>0.

Connected through hyperedge HE-1 (induced by source paper "adversarially robust low dimensional representations"):
  with members:
    - "vc classes are adversarially robustly learnable but only improperly"
    - "how hard is robust mean estimation"
    - "optimal average case reductions to sparse pca from weak assumptions to strong hardness"
    - "on robustness to adversarial examples and polynomial optimization"
    - "approximating operator norms via generalized krivine rounding"
    - "a spectral view of adversarially robust features"
    - "high dimensional robust mean estimation in nearly linear time"
    - "high accuracy low precision training"

Connected through hyperedge HE-2 (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"
    - "variable selection is hard"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "art of singular vectors and universal adversarial perturbations"):
  with members:
    - "adversarial perturbations against deep neural networks for malware classification"
    - "technical report on the cleverhans v2 1 0 adversarial examples library"
    - "universal adversarial perturbations"
    - "delving into transferable adversarial examples and black box attacks"
    - "adversarial examples in the physical world"
    - "distillation as a defense to adversarial perturbations against deep neural networks"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"

Connected through hyperedge HE-4 (induced by source paper "online learning to rank with feedback at the top"):
  with members:
    - "online ranking with top 1 feedback"

Connected through hyperedge HE-5 (induced by source paper "hypercontractivity sum of squares proofs and their applications"):
  with members:
    - "analysis of boolean functions"
    - "rounding semidefinite programming hierarchies via global correlation"
    - "making the long code shorter with applications to the unique games conjecture"
    - "hypercontractivity and its applications"
    - "integrality gaps of linear and semi definite programming relaxations for knapsack"
    - "reductions between expansion problems"

Connected through hyperedge HE-6 (induced by source paper "grothendieck type inequalities in combinatorial optimization"):
  with members:
    - "the p versus np problem"
    - "sharp kernel clustering algorithms and their associated grothendieck inequalities"
    - "approximate kernel clustering"
    - "approximate graph coloring by semidefinite programming"

Connected through hyperedge HE-7 (induced by source paper "combinatorial geometry of graph partitioning i"):
  with members:
    - "measured descent a new embedding method for finite metrics"

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)