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: "memory sample tradeoffs for linear regression with small error"
  Abstract: We consider the problem of performing linear regression over a stream of $d$-dimensional examples, and show that any algorithm that uses a subquadratic amount of memory exhibits a slower rate of convergence than can be achieved without memory constraints. Specifically, consider a sequence of labeled examples $(a_1,b_1), (a_2,b_2)\ldots,$ with $a_i$ drawn independently from a $d$-dimensional isotropic Gaussian, and where $b_i = \langle a_i, x\rangle + \eta_i,$ for a fixed $x \in \mathbb{R}^d$ with $\|x\|_2 = 1$ and with independent noise $\eta_i$ drawn uniformly from the interval $[-2^{-d/5},2^{-d/5}].$ We show that any algorithm with at most $d^2/4$ bits of memory requires at least $\Omega(d \log \log \frac{1}{\epsilon})$ samples to approximate $x$ to $\ell_2$ error $\epsilon$ with probability of success at least $2/3$, for $\epsilon$ sufficiently small as a function of $d$. In contrast, for such $\epsilon$, $x$ can be recovered to error $\epsilon$ with probability $1-o(1)$ with memory $O\left(d^2 \log(1/\epsilon)\right)$ using $d$ examples. This represents the first nontrivial lower bounds for regression with super-linear memory, and may open the door for strong memory/sample tradeoffs for continuous optimization.

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:
    - "communication and memory efficient testing of discrete distributions"
    - "detecting correlations with little memory and communication"
    - "extractor based time space lower bounds for learning"
    - "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 "towards a combinatorial characterization of bounded memory learning"):
  with members:
    - "space lower bounds for linear prediction in the streaming model"
    - "detecting correlations with little memory and communication"
    - "extractor based time space lower bounds for learning"
    - "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-3 (induced by source paper "space lower bounds for linear prediction"):
  with members:
    - "testing matrix rank optimally"
    - "extractor based time space lower bounds for learning"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "space lower bounds for linear prediction in the streaming model"):
  with members:
    - "learning without interaction requires separation"
    - "detecting correlations with little memory and communication"
    - "testing matrix rank optimally"
    - "extractor based time space lower bounds for learning"
    - "time space tradeoffs for learning from small test spaces learning low degree polynomial functions"
    - "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"

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)