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 "time space tradeoffs for distinguishing distributions and applications to security of goldreich s prg"):
  Abstract: In this work, we establish lower-bounds against memory bounded algorithms for distinguishing between natural pairs of related distributions from samples that arrive in a streaming setting. #R##N#In our first result, we show that any algorithm that distinguishes between uniform distribution on $\{0,1\}^n$ and uniform distribution on an $n/2$-dimensional linear subspace of $\{0,1\}^n$ with non-negligible advantage needs $2^{\Omega(n)}$ samples or $\Omega(n^2)$ memory. #R##N#Our second result applies to distinguishing outputs of Goldreich's local pseudorandom generator from the uniform distribution on the output domain. Specifically, Goldreich's pseudorandom generator $G$ fixes a predicate $P:\{0,1\}^k \rightarrow \{0,1\}$ and a collection of subsets $S_1, S_2, \ldots, S_m \subseteq [n]$ of size $k$. For any seed $x \in \{0,1\}^n$, it outputs $P(x_{S_1}), P(x_{S_2}), \ldots, P(x_{S_m})$ where $x_{S_i}$ is the projection of $x$ to the coordinates in $S_i$. We prove that whenever $P$ is $t$-resilient (all non-zero Fourier coefficients of $(-1)^P$ are of degree $t$ or higher), then no algorithm, with $<n^\epsilon$ memory, can distinguish the output of $G$ from the uniform distribution on $\{0,1\}^m$ with a large inverse polynomial advantage, for stretch $m \le \left(\frac{n}{t}\right)^{\frac{(1-\epsilon)}{36}\cdot t}$ (barring some restrictions on $k$). The lower bound holds in the streaming model where at each time step $i$, $S_i\subseteq [n]$ is a randomly chosen (ordered) subset of size $k$ and the distinguisher sees either $P(x_{S_i})$ or a uniformly random bit along with $S_i$. #R##N#Our proof builds on the recently developed machinery for proving time-space trade-offs (Raz 2016 and follow-ups) for search/learning problems.

Includes members:
  Member "memory sample tradeoffs for linear regression with small error":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards a combinatorial characterization of bounded memory learning")
  Member "communication and memory efficient testing of discrete distributions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pan private uniformity testing")
  Member "detecting correlations with little memory and communication":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards a combinatorial characterization of bounded memory learning")
  Member "extractor based time space lower bounds for learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "poly time universality and limitations of deep learning")
  Member "mixing complexity and its applications to neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a general memory bounded learning algorithm")
  Member "fast learning requires good memory a time space lower bound for parity learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "towards a combinatorial characterization of bounded memory learning")
  Member "information theoretically secure databases":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fast learning requires good memory a time space lower bound for parity learning")
  Member "strongly refuting random csps below the spectral threshold":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sum of squares meets square loss fast rates for agnostic tensor completion")

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.CC(Computational Complexity)