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: "memory based online learning of deep representations from video streams"
  Abstract: We present a novel online unsupervised method for face identity learning from video streams. The method exploits deep face descriptors together with a memory based learning mechanism that takes advantage of the temporal coherence of visual data. Specifically, we introduce a discriminative feature matching solution based on Reverse Nearest Neighbour and a feature forgetting strategy that detect redundant features and discard them appropriately while time progresses. It is shown that the proposed learning procedure is asymptotically stable and can be effectively used in relevant applications like multiple face identification and tracking from unconstrained video streams. Experimental results show that the proposed method achieves comparable results in the task of multiple face tracking and better performance in face identification with offline approaches exploiting future information. Code will be publicly available.

Connected through hyperedge HE-1 (induced by source paper "robust visual object tracking with natural language region proposal network"):
  with members:
    - "tell me what to track"
    - "language features matter effective language representations for vision language tasks"
    - "a fast and accurate one stage approach to visual grounding"
    - "universal sentence encoder"
    - "siamrpn evolution of siamese visual tracking with very deep networks"
    - "vital visual tracking via adversarial learning"
    - "end to end representation learning for correlation filter based tracking"
    - "learning two branch neural networks for image text matching tasks"

Connected through hyperedge HE-2 (induced by source paper "task free continual learning"):
  with members:
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "experience replay for continual learning"
    - "online deep learning growing rbm on the fly"
    - "exploring the challenges towards lifelong fact learning"
    - "online deep metric learning"
    - "rainbow combining improvements in deep reinforcement learning"
    - "unsupervised incremental learning of deep descriptors from video streams"
    - "memory aware synapses learning what not to forget"

Connected through hyperedge HE-3 (induced by source paper "can composite appearance network and a novel evaluation metric for person tracking"):
  with members:
    - "online multi object tracking with dual matching attention networks"
    - "quality aware network for set to set recognition"
    - "jointly attentive spatial temporal pooling networks for video based person re identification"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "multi camera multi object tracking"
    - "neural aggregation network for video face recognition"
    - "posetrack joint multi person pose estimation and tracking"
    - "learning a discriminative null space for person re identification"

Connected through hyperedge HE-4 (induced by source paper "can composite appearance network for person tracking and how to model errors in a tracking system"):
  with members:
    - "multi person articulated tracking with spatial and temporal embeddings"
    - "features for multi target multi camera tracking and re identification"
    - "quality aware network for set to set recognition"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "neural aggregation network for video face recognition"
    - "posetrack joint multi person pose estimation and tracking"
    - "learning a discriminative null space for person re identification"
    - "densely connected convolutional 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.CV(Computer Vision and Pattern Recognition)