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: "diamond sampling for approximate maximum all pairs dot product mad search"
  Abstract: Given two sets of vectors, A = {a1&#x2192;, . . . , am&#x2192;} and B = {b1&#x2192;, . . . , bn&#x2192;}, our problem is to find the top-t dot products, i.e., the largest |ai&#x2192; · bj&#x2192;| among all possible pairs. This is a fundamental mathematical problem that appears in numerous data applications involving similarity search, link prediction, and collaborative filtering. We propose a sampling-based approach that avoids direct computation of all mn dot products. We select diamonds (i.e., four-cycles) from the weighted tripartite representation of A and B. The probability of selecting a diamond corresponding to pair (i, j) is proportional to (ai&#x2192; · bj&#x2192;)2, amplifying the focus on the largest-magnitude entries. Experimental results indicate that diamond sampling is orders of magnitude faster than direct computation and requires far fewer samples than any competing approach. We also apply diamond sampling to the special case of maximum inner product search, and get significantly better results than the state-of-theart hashing methods.

Connected through hyperedge HE-1 (induced by source paper "interaction hard thresholding consistent sparse quadratic regression in sub quadratic time and space"):
  with members:
    - "high dimensional robust sparse regression"
    - "on the hardness of approximate and exact bichromatic maximum inner product"
    - "distributed pcp theorems for hardness of approximation in p"
    - "nonconvex sparse learning via stochastic optimization with progressive variance reduction"
    - "a greedy approach for budgeted maximum inner product search"
    - "on iterative hard thresholding methods for high dimensional m estimation"
    - "sparse polynomial learning and graph sketching"
    - "compressed matrix multiplication"

Connected through hyperedge HE-2 (induced by source paper "revisiting wedge sampling for budgeted maximum inner product search"):
  with members:
    - "a greedy approach for budgeted maximum inner product search"
    - "imagenet large scale visual recognition challenge"
    - "bpr bayesian personalized ranking from implicit feedback"

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"
    - "when hashes met wedges a distributed algorithm for finding high similarity vectors"
    - "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"
    - "supervised random walks predicting and recommending links in social networks"

Connected through hyperedge HE-4 (induced by source paper "when hashes met wedges a distributed algorithm for finding high similarity vectors"):
  with members:
    - "practical and optimal lsh for angular distance"
    - "the unified logging infrastructure for data analytics at twitter"
    - "complexity measures for map reduce and comparison to parallel computing"
    - "dimension independent similarity computation"
    - "compressed matrix multiplication"
    - "low rank matrix valued chernoff bounds and approximate matrix multiplication"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"
    - "on sparse representations of linear operators and the approximation of matrix products"

Connected through hyperedge HE-5 (induced by source paper "deep coevolutionary network embedding user and item features for recommendation"):
  with members:
    - "improved recurrent neural networks for session based recommendations"
    - "general factorization framework for context aware recommendations"
    - "discriminative embeddings of latent variable models for structured data"
    - "session based recommendations with recurrent neural networks"
    - "dynamic poisson factorization"
    - "adam a method for stochastic optimization"
    - "collaborative deep learning for recommender systems"

Connected through hyperedge HE-6 (induced by source paper "a greedy approach for budgeted maximum inner product search"):
  with members:
    - "clustering is efficient for approximate maximum inner product search"
    - "a scalable asynchronous distributed algorithm for topic modeling"
    - "large scale multi label learning with missing labels"

Connected through hyperedge HE-7 (induced by source paper "trigger detection for adaptive scientific workflows using percentile sampling"):
  with members:
    - "counting triangles in massive graphs with mapreduce"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "wedge sampling for computing clustering coefficients and triangle counts on large graphs"
    - "triadic measures on graphs the power of wedge sampling"

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)