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: "extractor based time space lower bounds for learning"
  Abstract: A matrix $M: A \times X \rightarrow \{-1,1\}$ corresponds to the following learning problem: An unknown element $x \in X$ is chosen uniformly at random. A learner tries to learn $x$ from a stream of samples, $(a_1, b_1), (a_2, b_2) \ldots$, where for every $i$, $a_i \in A$ is chosen uniformly at random and $b_i = M(a_i,x)$. #R##N#Assume that $k,\ell, r$ are such that any submatrix of $M$ of at least $2^{-k} \cdot |A|$ rows and at least $2^{-\ell} \cdot |X|$ columns, has a bias of at most $2^{-r}$. We show that any learning algorithm for the learning problem corresponding to $M$ requires either a memory of size at least $\Omega\left(k \cdot \ell \right)$, or at least $2^{\Omega(r)}$ samples. The result holds even if the learner has an exponentially small success probability (of $2^{-\Omega(r)}$). #R##N#In particular, this shows that for a large class of learning problems, any learning algorithm requires either a memory of size at least $\Omega\left((\log |X|) \cdot (\log |A|)\right)$ or an exponential number of samples, achieving a tight $\Omega\left((\log |X|) \cdot (\log |A|)\right)$ lower bound on the size of the memory, rather than a bound of $\Omega\left(\min\left\{(\log |X|)^2,(\log |A|)^2\right\}\right)$ obtained in previous works [R17,MM17b]. #R##N#Moreover, our result implies all previous memory-samples lower bounds, as well as a number of new applications. #R##N#Our proof builds on [R17] that gave a general technique for proving memory-samples lower bounds.

Connected through hyperedge HE-1 (induced by source paper "time space tradeoffs for distinguishing distributions and applications to security of goldreich s prg"):
  with members:
    - "memory sample tradeoffs for linear regression with small error"
    - "communication and memory efficient testing of discrete distributions"
    - "detecting correlations with little memory and communication"
    - "mixing complexity and its applications to neural networks"
    - "fast learning requires good memory a time space lower bound for parity learning"
    - "information theoretically secure databases"
    - "strongly refuting random csps below the spectral threshold"
    - "how to refute a random csp"

Connected through hyperedge HE-2 (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"
    - "time space tradeoffs for learning from small test spaces learning low degree polynomial functions"
    - "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-3 (induced by source paper "towards a combinatorial characterization of bounded memory learning"):
  with members:
    - "memory sample tradeoffs for linear regression with small error"
    - "space lower bounds for linear prediction in the streaming model"
    - "detecting correlations with little memory and communication"
    - "a general memory bounded learning algorithm"
    - "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"
    - "a complete characterization of statistical query learning with applications to evolvability"

Connected through hyperedge HE-4 (induced by source paper "space efficient approximation to maximum matching size from uniform edge samples"):
  with members:
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"
    - "weighted matchings via unweighted augmentations"
    - "coresets meet edcs algorithms for matching and vertex cover on massive graphs"
    - "estimating graph parameters from random order streams"
    - "testable bounded degree graph properties are random order streamable"
    - "on estimating maximum matching size in graph streams"
    - "a 2 ϵ approximation for maximum weight matching in the semi streaming model"
    - "the sparse awakens streaming algorithms for matching size estimation in sparse graphs"

Connected through hyperedge HE-5 (induced by source paper "accuracy memory tradeoffs and phase transitions in belief propagation"):
  with members:
    - "the circuit complexity of inference"
    - "communication complexity of estimating correlations"
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "reducibility and computational lower bounds for problems with planted sparse structure"
    - "inference under information constraints i lower bounds from chi square contraction"
    - "secret key generation with limited interaction"
    - "fast learning requires good memory a time space lower bound for parity learning"
    - "information theoretic lower bounds for distributed function computation"

Connected through hyperedge HE-6 (induced by source paper "top down induction of decision trees rigorous guarantees and inherent limitations"):
  with members:
    - "on the optimality of trees generated by id3"
    - "beyond the low degree algorithm mixtures of subcubes and their applications"
    - "hyperparameter optimization a spectral approach"
    - "fast learning requires good memory a time space lower bound for parity learning"
    - "analysis of boolean functions"
    - "learning circuits with few negations"
    - "fundamental limits of online and distributed algorithms for statistical learning and estimation"
    - "the average sensitivity of an intersection of half spaces"

Connected through hyperedge HE-7 (induced by source paper "memory sample tradeoffs for linear regression with small error"):
  with members:
    - "parallelization does not accelerate convex optimization adaptivity lower bounds for non smooth convex minimization"
    - "detecting correlations with little memory and communication"
    - "fast learning requires good memory a time space lower bound for parity learning"
    - "communication lower bounds for statistical estimation problems via a distributed data processing inequality"
    - "fundamental limits of online and distributed algorithms for statistical learning and estimation"
    - "efficient accelerated coordinate descent methods and faster algorithms for solving linear systems"
    - "on the complexity of bandit and derivative free stochastic convex optimization"
    - "a concentration theorem for projections"

Connected through hyperedge HE-8 (induced by source paper "space lower bounds for linear prediction"):
  with members:
    - "memory sample tradeoffs for linear regression with small error"
    - "testing matrix rank optimally"
    - "on communication complexity of classification problems"
    - "matrix norms in data streams faster multi pass and row order"
    - "optimal approximate matrix product in terms of stable rank"
    - "communication lower bounds for statistical estimation problems via a distributed data processing inequality"
    - "on the communication complexity of linear algebraic problems in the message passing model"

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)