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: "time space tradeoffs for learning from small test spaces learning low degree polynomial functions"
  Abstract: We develop an extension of recently developed methods for obtaining time-space tradeoff lower bounds for problems of learning from random test samples to handle the situation where the space of tests is signficantly smaller than the space of inputs, a class of learning problems that is not handled by prior work. This extension is based on a measure of how matrices amplify the 2-norms of probability distributions that is more refined than the 2-norms of these matrices. #R##N#As applications that follow from our new technique, we show that any algorithm that learns $m$-variate homogeneous polynomial functions of degree at most $d$ over $\mathbb{F}_2$ from evaluations on randomly chosen inputs either requires space $\Omega(mn)$ or $2^{\Omega(m)}$ time where $n=m^{\Theta(d)}$ is the dimension of the space of such functions. These bounds are asymptotically optimal since they match the tradeoffs achieved by natural learning algorithms for the problems.

Connected through hyperedge HE-1 (induced by source paper "poly time universality and limitations of deep learning"):
  with members:
    - "gradient descent for one hidden layer neural networks polynomial convergence and sq lower bounds"
    - "provable limitations of deep learning"
    - "on the complexity of learning neural networks"
    - "extractor based time space lower bounds for learning"
    - "understanding deep learning requires rethinking generalization"
    - "distribution specific hardness of learning neural networks"
    - "statistical query algorithms for mean vector estimation and stochastic convex optimization"
    - "train faster generalize better stability of stochastic gradient descent"

Connected through hyperedge HE-2 (induced by source paper "space lower bounds for linear prediction in the streaming model"):
  with members:
    - "memory sample tradeoffs for linear regression with small error"
    - "learning without interaction requires separation"
    - "detecting correlations with little memory and communication"
    - "testing matrix rank optimally"
    - "extractor based time space lower bounds for learning"
    - "fast learning requires good memory a time space lower bound for parity learning"
    - "on the communication complexity of linear algebraic problems in the message passing model"

Connected through hyperedge HE-3 (induced by source paper "provable limitations of deep learning"):
  with members:
    - "gradient descent for one hidden layer neural networks polynomial convergence and sq lower bounds"
    - "extractor based time space lower bounds for learning"
    - "understanding deep learning requires rethinking generalization"
    - "distribution specific hardness of learning neural networks"
    - "statistical query algorithms for mean vector estimation and stochastic convex optimization"
    - "train faster generalize better stability of stochastic gradient descent"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"

Connected through hyperedge HE-4 (induced by source paper "detecting correlations with little memory and communication"):
  with members:
    - "extractor based time space lower bounds for learning"
    - "communication lower bounds for statistical estimation problems via a distributed data processing inequality"

Connected through hyperedge HE-5 (induced by source paper "a general memory bounded learning algorithm"):
  with members:
    - "extractor based time space lower bounds for learning"
    - "mixing complexity and its applications to neural networks"
    - "a general characterization of the statistical query complexity"
    - "fast learning requires good memory a time space lower bound for parity learning"
    - "fundamental limits of online and distributed algorithms for statistical learning and estimation"

Connected through hyperedge HE-6 (induced by source paper "extractor based time space lower bounds for learning"):
  with members:
    - "mixing complexity and its applications to neural networks"
    - "fast learning requires good memory a time space lower bound for parity learning"
    - "fundamental limits of online and distributed algorithms for statistical learning and estimation"

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.LG(Machine Learning)