human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "interaction hard thresholding consistent sparse quadratic regression in sub quadratic time and space"):
  Abstract: Quadratic regression involves modeling the response as a (generalized) linear function of not only the features $x^{j_1}$ but also of quadratic terms $x^{j_1}x^{j_2}$. The inclusion of such higher-order "interaction terms" in regression often provides an easy way to increase accuracy in already-high-dimensional problems. However, this explodes the problem dimension from linear $O(p)$ to quadratic $O(p^2)$, and it is common to look for sparse interactions (typically via heuristics). In this paper, we provide a new algorithm - Interaction Hard Thresholding (IntHT) which is the first one to provably accurately solve this problem in sub-quadratic time and space. It is a variant of Iterative Hard Thresholding; one that uses the special quadratic structure to devise a new way to (approx.) extract the top elements of a $p^2$ size gradient in sub-$p^2$ time and space. Our main result is to theoretically prove that, in spite of the many speedup-related approximations, IntHT linearly converges to a consistent estimate under standard high-dimensional sparse recovery assumptions. We also demonstrate its value via synthetic experiments. Moreover, we numerically show that IntHT can be extended to higher-order regression problems, and also theoretically analyze an SVRG variant of IntHT.

Includes members:
  Member "high dimensional robust sparse regression":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks")
  Member "on the hardness of approximate and exact bichromatic maximum inner product":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "approximating subset sum is equivalent to min plus convolution")
  Member "distributed pcp theorems for hardness of approximation in p":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "graphs cannot be indexed in polynomial time for sub quadratic time string matching unless seth fails")
  Member "nonconvex sparse learning via stochastic optimization with progressive variance reduction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "vr sgd a simple stochastic variance reduction method for machine learning")
  Member "a greedy approach for budgeted maximum inner product search":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a 3 accelerating attention mechanisms in neural networks with approximation")
  Member "diamond sampling for approximate maximum all pairs dot product mad search":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "revisiting wedge sampling for budgeted maximum inner product search")
  Member "on iterative hard thresholding methods for high dimensional m estimation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "soft threshold weight reparameterization for learnable sparsity")
  Member "sparse polynomial learning and graph sketching":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "hyperparameter optimization a spectral approach")

Question: Please tell me which class the source paper 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.LG(Machine Learning)