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: "fast learning requires good memory a time space lower bound for parity learning"
  Abstract: We prove that any algorithm for learning parities requires either a memory of quadratic size or an exponential number of samples. This proves a recent conjecture of Steinhardt, Valiant and Wager and shows that for some learning problems a large storage space is crucial. #R##N#More formally, in the problem of parity learning, an unknown string $x \in \{0,1\}^n$ was 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 each~$a_t$ is uniformly distributed over $\{0,1\}^n$ and $b_t$ is the inner product of $a_t$ and $x$, modulo~2. We show that any algorithm for parity learning, that uses less than $\frac{n^2}{25}$ bits of memory, requires an exponential number of samples. #R##N#Previously, there was no non-trivial lower bound on the number of samples needed, for any learning problem, even if the allowed memory size is $O(n)$ (where $n$ is the space needed to store one sample). #R##N#We also give an application of our result in the field of bounded-storage cryptography. We show an encryption scheme that requires a private key of length $n$, as well as time complexity of $n$ per encryption/decription of each bit, and is provenly and unconditionally secure as long as the attacker uses less than $\frac{n^2}{25}$ memory bits and the scheme is used at most an exponential number of times. Previous works on bounded-storage cryptography assumed that the memory size used by the attacker is at most linear in the time needed for encryption/decription.

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"
    - "extractor based time space lower bounds for learning"
    - "mixing complexity and its applications to neural networks"
    - "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:
    - "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"
    - "extractor based time space lower bounds for learning"
    - "a general memory bounded learning algorithm"
    - "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 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"
    - "extractor based time space lower bounds for learning"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "distributed learning with sublinear communication"):
  with members:
    - "introduction to online convex optimization"
    - "space lower bounds for linear prediction"
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "communication compression for decentralized training"
    - "detecting correlations with little memory and communication"
    - "distributed simulation and distributed inference"
    - "sparsified sgd with memory"
    - "inference under information constraints i lower bounds from chi square contraction"

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"
    - "extractor based time space lower bounds for learning"
    - "secret key generation with limited interaction"
    - "information theoretic lower bounds for distributed function computation"

Connected through hyperedge HE-6 (induced by source paper "estimating entropy of distributions in constant space"):
  with members:
    - "communication and memory efficient testing of discrete distributions"
    - "efficient profile maximum likelihood for universal symmetric property estimation"
    - "detecting correlations with little memory and communication"
    - "minimax optimal estimators for additive scalar functionals of discrete distributions"
    - "an optimal algorithm for l1 heavy hitters in insertion streams and related problems"
    - "minimax rate optimal estimation of divergences between discrete distributions"
    - "minimax estimation of functionals of discrete distributions"
    - "minimax rates of entropy estimation on large alphabets via best polynomial approximation"

Connected through hyperedge HE-7 (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"
    - "extractor based time space lower bounds for learning"
    - "hyperparameter optimization a spectral approach"
    - "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-8 (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"
    - "extractor based time space lower bounds for 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"

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)