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: "when hashes met wedges a distributed algorithm for finding high similarity vectors"
  Abstract: Finding similar user pairs is a fundamental task in social networks, with numerous applications in ranking and personalization tasks such as link prediction and tie strength detection. A common manifestation of user similarity is based upon network structure: each user is represented by a vector that represents the user's network connections, where pairwise cosine similarity among these vectors defines user similarity. The predominant task for user similarity applications is to discover all similar pairs that have a pairwise cosine similarity value larger than a given threshold τ. In contrast to previous work where τ is assumed to be quite close to 1, we focus on recommendation applications where τ is small, but still meaningful. The all pairs cosine similarity problem is computationally challenging on networks with billions of edges, and especially so for settings with small τ. To the best of our knowledge, there is no practical solution for computing all user pairs with, say τ = 0.2 on large social networks, even using the power of distributed algorithms.#R##N##R##N#Our work directly addresses this challenge by introducing a new algorithm --- WHIMP --- that solves this problem efficiently in the MapReduce model. The key insight in WHIMP is to combine the ``wedge-sampling" approach of Cohen-Lewis for approximate matrix multiplication with the SimHash random projection techniques of Charikar. We provide a theoretical analysis of WHIMP, proving that it has near optimal communication costs while maintaining computation cost comparable with the state of the art. We also empirically demonstrate WHIMP's scalability by computing all highly similar pairs on four massive data sets, and show that it accurately finds high similarity pairs. In particular, we note that WHIMP successfully processes the entire Twitter network, which has tens of billions of edges.

Connected through hyperedge HE-1 (induced by source paper "lsf join locality sensitive filtering for distributed all pairs set similarity under skew"):
  with members:
    - "hardness of bichromatic closest pair with jaccard similarity"
    - "set similarity search for skewed data"
    - "adaptive mapreduce similarity joins"
    - "scalable and robust set similarity join"
    - "a framework for similarity search with space time tradeoffs using locality sensitive filtering"
    - "on the complexity of inner product similarity join"
    - "lsh ensemble internet scale domain search"
    - "massively parallel similarity join edge isoperimetry and distance correlations on the hypercube"

Connected through hyperedge HE-2 (induced by source paper "race sub linear memory sketches for approximate near neighbor search on streaming data"):
  with members:
    - "approximate nearest neighbors in limited space"
    - "hashing based estimators for kernel density in high dimensions"
    - "billion scale similarity search with gpus"
    - "near optimal euclidean metric compression"
    - "practical and optimal lsh for angular distance"
    - "locality sensitive hashing without false negatives"
    - "beyond locality sensitive hashing"
    - "new constructions of rip matrices with fast multiplication and fewer rows"

Connected through hyperedge HE-3 (induced by source paper "simplicial closure and higher order link prediction"):
  with members:
    - "graph embedding techniques applications and performance a survey"
    - "motifs in temporal networks"
    - "cross validation estimate of the number of clusters in a network"
    - "higher order organization of complex networks"
    - "node2vec scalable feature learning for networks"
    - "representing higher order dependencies in networks"
    - "diamond sampling for approximate maximum all pairs dot product mad search"
    - "supervised random walks predicting and recommending links in social networks"

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.SI(Social and Information Networks)