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: "bloom filters in adversarial environments"
  Abstract: Many efficient data structures use randomness, allowing them to improve upon deterministic ones. Usually, their efficiency and/or correctness are analyzed using probabilistic tools under the assumption that the inputs and queries are independent of the internal randomness of the data structure. In this work, we consider data structures in a more robust model, which we call the adversarial model. Roughly speaking, this model allows an adversary to choose inputs and queries adaptively according to previous responses. Specifically, we consider a data structure known as "Bloom filter" and prove a tight connection between Bloom filters in this model and cryptography. #R##N#A Bloom filter represents a set $S$ of elements approximately, by using fewer bits than a precise representation. The price for succinctness is allowing some errors: for any $x \in S$ it should always answer 'Yes', and for any $x \notin S$ it should answer 'Yes' only with small probability. #R##N#In the adversarial model, we consider both efficient adversaries (that run in polynomial time) and computationally unbounded adversaries that are only bounded in the amount of queries they can make. For computationally bounded adversaries, we show that non-trivial (memory-wise) Bloom filters exist if and only if one-way functions exist. For unbounded adversaries we show that there exists a Bloom filter for sets of size $n$ and error \varepsilon, that is secure against t queries and uses only $O(n \log{\frac{1}{\varepsilon}} + t)$ bits of memory. In comparison, $n\log{\frac{1}{\varepsilon}}$ is the best possible under a non-adaptive adversary.

Connected through hyperedge HE-1 (induced by source paper "the adversarial robustness of sampling"):
  with members:
    - "vc classes are adversarially robustly learnable but only improperly"
    - "introduction to online convex optimization"
    - "weighted reservoir sampling from distributed streams"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "stochastic bandits robust to adversarial corruptions"
    - "the everlasting database statistical validity at a fair price"
    - "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions"
    - "adaptive online learning in dynamic environments"

Connected through hyperedge HE-2 (induced by source paper "data structures meet cryptography 3sum with preprocessing"):
  with members:
    - "the strong 3sum indexing conjecture is false"
    - "equivalence of systematic linear data structures and matrix rigidity"
    - "lower bounds for oblivious data structures"
    - "static data structure lower bounds imply rigidity"
    - "how hard is it to find honest witnesses"
    - "conditional lower bounds for space time tradeoffs"
    - "orthogonal vectors indexing"
    - "clustered integer 3sum via additive combinatorics"

Connected through hyperedge HE-3 (induced by source paper "a model for learned bloom filters and optimizing by sandwiching"):
  with members:
    - "optimizing learned bloom filters by sandwiching"
    - "a model for learned bloom filters and related structures"
    - "the case for learned index structures"
    - "adaptive cuckoo filters"
    - "bloomier filters a second look"

Connected through hyperedge HE-4 (induced by source paper "quotient hash tables efficiently detecting duplicates in streaming data"):
  with members:
    - "optimal lower bounds for universal relation and for samplers and finding duplicates in streams"
    - "tight bounds for lp samplers finding duplicates in streams and related problems"

Connected through hyperedge HE-5 (induced by source paper "sampling and reconstruction using bloom filters"):
  with members:
    - "bloofi multidimensional bloom filters"
    - "can cascades be predicted"
    - "tight bounds for lp samplers finding duplicates in streams and related problems"
    - "a framework for quantitative analysis of cascades on networks"

Connected through hyperedge HE-6 (induced by source paper "bloom filters adaptivity and the dictionary problem"):
  with members:
    - "adaptive cuckoo filters"
    - "fast and powerful hashing using tabulation"
    - "how robust are linear sketches to adaptive inputs"
    - "don t thrash how to cache your hash on flash"
    - "backyard cuckoo hashing constant worst case operations with a succinct representation"
    - "an optimal bloom filter replacement based on matrix solving"

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.CR(Cryptography and Security)