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 "bloom filters adaptivity and the dictionary problem"):
  Abstract: The Bloom filter---or, more generally, an approximate membership query data structure (AMQ)---maintains a compact, probabilistic representation of a set S of keys from a universe U. An AMQ supports lookups, inserts, and (for some AMQs) deletes. A query for an x in S is guaranteed to return "present." A query for x not in S returns "absent" with probability at least 1-epsilon, where epsilon is a tunable false positive probability. If a query returns "present," but x is not in S, then x is a false positive of the AMQ. Because AMQs have a nonzero probability of false-positives, they require far less space than explicit set representations. #R##N#AMQs are widely used to speed up dictionaries that are stored remotely (e.g., on disk/across a network). Most AMQs offer weak guarantees on the number of false positives they will return on a sequence of queries. The false-positive probability of epsilon holds only for a single query. It is easy for an adversary to drive an AMQ's false-positive rate towards 1 by simply repeating false positives. #R##N#This paper shows what it takes to get strong guarantees on the number of false positives. We say that an AMQs is adaptive if it guarantees a false-positive probability of epsilon for every query, regardless of answers to previous queries. First, we prove that it is impossible to build a small adaptive AMQ, even when the AMQ is immediately told whenever it returns a false positive. We then show how to build an adaptive AMQ that partitions its state into a small local component and a larger remote component. In addition to being adaptive, the local component of our AMQ dominates existing AMQs in all regards. It uses optimal space up to lower-order terms and supports queries and updates in worst-case constant time, with high probability. Thus, we show that adaptivity has no cost.

Includes members:
  Member "adaptive cuckoo filters":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fully dynamic space efficient dictionaries and filters with constant number of memory accesses")
  Member "fast and powerful hashing using tabulation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "algorithms for similarity search and pseudorandomness")
  Member "bloom filters in adversarial environments":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the adversarial robustness of sampling")
  Member "how robust are linear sketches to adaptive inputs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "span recovery for deep neural networks with applications to input obfuscation")
  Member "don t thrash how to cache your hash on flash":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "lsm based storage techniques a survey")
  Member "backyard cuckoo hashing constant worst case operations with a succinct representation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fully dynamic space efficient dictionaries and filters with constant number of memory accesses")
  Member "an optimal bloom filter replacement based on matrix solving":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fully dynamic space efficient dictionaries and filters with constant number of memory accesses")

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.DS(Data Structures and Algorithms)