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 "ell_p box admm a versatile framework for integer programming"):
  Abstract: This paper revisits the integer programming (IP) problem, which plays a fundamental role in many computer vision and machine learning applications. The literature abounds with many seminal works that address this problem, some focusing on continuous approaches (e.g. linear program relaxation) while others on discrete ones (e.g., min-cut). However, a limited number of them are designed to handle the general IP form and even these methods cannot adequately satisfy the simultaneous requirements of accuracy, feasibility, and scalability. To this end, we propose a novel and versatile framework called $\ell_p$-box ADMM, which is based on two parts. (1) The discrete constraint is equivalently replaced by the intersection of a box and a $(n-1)$-dimensional sphere (defined through the $\ell_p$ norm). (2) We infuse this equivalence into the ADMM (Alternating Direction Method of Multipliers) framework to handle these continuous constraints separately and to harness its attractive properties. More importantly, the ADMM update steps can lead to manageable sub-problems in the continuous domain. To demonstrate its efficacy, we consider an instance of the framework, namely $\ell_2$-box ADMM applied to binary quadratic programming (BQP). Here, the ADMM steps are simple, computationally efficient, and theoretically guaranteed to converge to a KKT point. We demonstrate the applicability of $\ell_2$-box ADMM on three important applications: MRF energy minimization, graph matching, and clustering. Results clearly show that it significantly outperforms existing generic IP solvers both in runtime and objective. It also achieves very competitive performance vs. state-of-the-art methods specific to these applications.

Includes members:
  Member "tracklet association by online target specific metric learning and coherent dynamics estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online multiple pedestrian tracking using deep temporal appearance matching association")
  Member "large scale binary quadratic optimization using semidefinite relaxation and applications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hashing with binary matrix pursuit")
  Member "improving image restoration with soft rounding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "image correction via deep reciprocating hdr transformation")
  Member "a fast semidefinite approach to solving binary quadratic problems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "convex optimisation for inverse kinematics")
  Member "an alternating direction algorithm for matrix completion with nonnegative factors":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a group norm regularized lrr factorization model for spectral clustering")
  Member "learning with submodular functions a convex optimization perspective":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "online non monotone dr submodular maximization")

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.CV(Computer Vision and Pattern Recognition)