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 "hyperlink regression via bregman divergence"):
  Abstract: A collection of $U \: (\in \mathbb{N})$ data vectors is called a $U$-tuple, and the association strength among the vectors of a tuple is termed as the hyperlink weight, that is assumed to be symmetric with respect to permutation of the entries in the index. We herein propose Bregman hyperlink regression (BHLR), which learns a user-specified symmetric similarity function such that it predicts the tuple's hyperlink weight from data vectors stored in the $U$-tuple. Nonlinear functions, such as neural networks, can be employed for the similarity function. BHLR is based on Bregman divergence (BD) and encompasses various existing methods such as logistic regression ($U=1$), Poisson regression ($U=1$), graph embedding ($U=2$), matrix factorization ($U=2$), tensor factorization ($U \geq 2$), and their variants equipped with arbitrary BD. We demonstrate that, regardless of the choice of BD and $U \in \mathbb{N}$, the proposed BHLR is generally (P-1) robust against the distributional misspecification, that is, it asymptotically recovers the underlying true conditional expectation of hyperlink weights given data vectors regardless of its conditional distribution, and (P-2) computationally tractable, that is, it is efficiently computed by stochastic optimization algorithms using a novel generalized minibatch sampling procedure for hyper-relational data. Furthermore, a theoretical guarantee for the optimization is presented. Numerical experiments demonstrate the promising performance of the proposed BHLR.

Includes members:
  Member "representation learning with weighted inner product for universal approximation of general similarities":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sample drop detection for distant speech recognition with asynchronous devices distributed in space")
  Member "learning continuous hierarchies in the lorentz model of hyperbolic geometry":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discriminative topic mining via category name guided text embedding")
  Member "how to escape saddle points efficiently":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "understanding global loss landscape of one hidden layer relu neural networks")
  Member "an overview of gradient descent optimization algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hyper parameter optimization a review of algorithms and applications")
  Member "line large scale information network embedding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial attacks and defenses on graphs a review and empirical study")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "a survey on metric learning for feature vectors and structured data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an overview of distance and similarity functions for structured data")
  Member "distributed representations of words and phrases and their compositionality":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")

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.SI(Social and Information Networks)