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: "succinct data structures for retrieval and approximate membership"
  Abstract: The retrieval problem is the problem of associating data with keys in a set. Formally, the data structure must store a function f: U ->{0,1}^r that has specified values on the elements of a given set S, a subset of U, |S|=n, but may have any value on elements outside S. Minimal perfect hashing makes it possible to avoid storing the set S, but this induces a space overhead of Theta(n) bits in addition to the nr bits needed for function values. In this paper we show how to eliminate this overhead. Moreover, we show that for any k query time O(k) can be achieved using space that is within a factor 1+e^{-k} of optimal, asymptotically for large n. If we allow logarithmic evaluation time, the additive overhead can be reduced to O(log log n) bits whp. The time to construct the data structure is O(n), expected. A main technical ingredient is to utilize existing tight bounds on the probability of almost square random matrices with rows of low weight to have full row rank. In addition to direct constructions, we point out a close connection between retrieval structures and hash tables where keys are stored in an array and some kind of probing scheme is used. Further, we propose a general reduction that transfers the results on retrieval into analogous results on approximate membership, a problem traditionally addressed using Bloom filters. Again, we show how to eliminate the space overhead present in previously known methods, and get arbitrarily close to the lower bound. The evaluation procedures of our data structures are extremely simple (similar to a Bloom filter). For the results stated above we assume free access to fully random hash functions. However, we show how to justify this assumption using extra space o(n) to simulate full randomness on a RAM.

Connected through hyperedge HE-1 (induced by source paper "meta learning neural bloom filters"):
  with members:
    - "optimizing learned bloom filters by sandwiching"
    - "learning attractor dynamics for generative memory"
    - "a model for learned bloom filters and related structures"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "in datacenter performance analysis of a tensor processing unit"
    - "the case for learned index structures"
    - "device placement optimization with reinforcement learning"
    - "learning to remember rare events"

Connected through hyperedge HE-2 (induced by source paper "fully dynamic space efficient dictionaries and filters with constant number of memory accesses"):
  with members:
    - "bloom filters adaptivity and the dictionary problem"
    - "adaptive cuckoo filters"
    - "fast scalable construction of minimal perfect hash functions"
    - "dynamic integer sets with optimal rank select and predecessor search"
    - "how to approximate a set without knowing its size in advance"
    - "don t thrash how to cache your hash on flash"
    - "the power of simple tabulation hashing"
    - "backyard cuckoo hashing constant worst case operations with a succinct representation"

Connected through hyperedge HE-3 (induced by source paper "constructing minimal perfect hash functions using sat technology"):
  with members:
    - "dense peelable random uniform hypergraphs"
    - "fast and scalable minimal perfect hashing for massive key sets"
    - "fast scalable construction of minimal perfect hash functions"

Connected through hyperedge HE-4 (induced by source paper "how to approximate a set without knowing its size in advance"):
  with members:
    - "sliding bloom filters"
    - "backyard cuckoo hashing constant worst case operations with a succinct representation"
    - "de amortized cuckoo hashing provable worst case performance and experimental results"
    - "an optimal bloom filter replacement based on matrix solving"

Connected through hyperedge HE-5 (induced by source paper "sliding bloom filters"):
  with members:
    - "how to approximate a set without knowing its size in advance"
    - "backyard cuckoo hashing constant worst case operations with a succinct representation"

Connected through hyperedge HE-6 (induced by source paper "don t rush into a union take time to find your roots"):
  with members:
    - "logarithmic lower bounds in the cell probe model"

Connected through hyperedge HE-7 (induced by source paper "backyard cuckoo hashing constant worst case operations with a succinct representation"):
  with members:
    - "sharp load thresholds for cuckoo hashing"
    - "tight thresholds for cuckoo hashing via xorsat"
    - "de amortized cuckoo hashing provable worst case performance and experimental results"
    - "an optimal bloom filter replacement based on matrix solving"
    - "efficient hashing with lookups in two memory accesses"

Connected through hyperedge HE-8 (induced by source paper "sharp load thresholds for cuckoo hashing"):
  with members:
    - "tight thresholds for cuckoo hashing via xorsat"
    - "de amortized cuckoo hashing provable worst case performance and experimental results"

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.DS(Data Structures and Algorithms)